Author H. Peter Alesso presents excerpts from his published portfolio and research projects. H. Peter Alesso Portfolio Past, Present, and Future. " Oh, why is love so complicated?" "It's not so complicated. You just have to love the other person more than yourself." Not everyone who fights is a warrior. A warrior knows what's worth fighting for.

Excerpt from the seventh book of the Henry Gallant Saga, Henry Gallant and the Great Ship. Henry Gallant and the Great Ship AMAZON Chapter 1 An Unfortunate Turn of Events As soon as the morning watch settled in, Captain Henry Gallant walked onto the Constellation’s bridge. The Officer-of-the-Deck rose and vacated the command chair without speaking. The voyage had lasted long enough for the crew to become accustomed to his routine. Habitually, during the first minutes of the day, he examined the ship’s vital operational parameters from his bedside monitor before going into CIC for a detailed task force sitrep. Blips from the combat space patrol (CSP) were visible on the main viewer. The speakers broadcast communication traffic from distant Hawkeyes. Once he had satisfied himself that all was as it should be, he appeared on the bridge and assessed the more mundane needs for the day. The OOD handed him a list of completed tasks and those that demanded his approval. During this activity, he was lost in contemplation, and no one dared interrupt his train of thought. Only after dictating his orders for the day did he relax and give a word of encouragement to the OOD. Then he disappeared below decks for his daily walkabout, where he gauged the temperament of the crew. The hour exercise through the spacecraft carrier allowed him to maintain his fitness. This ritual was the most efficient use of his time since it also allowed him to observe ongoing maintenance and repair activities. On the one hand, the number of administrative duties clamoring for his attention limited his time; on the other, keeping in sync with his ship’s pulse was vital to making good decisions. It brought a faint smile to his lips when he resolved to shift more of the clerical burden onto his XO. Margret Fletcher had a talent for paperwork and was known for her no-nonsense adherence to the regs. Even though he overloaded her of late, she had responded with her usual zeal. As he passed through compartment after compartment, he dictated audio notes into his comm pin about items that needed attention. He marched along the corridors and stepped through the open hatches, ever mindful of the crew’s attention. Although immersed in his process, the crew discerned that his military instincts were on full alert. He would notice the slightest failure of attention to detail as the men and women went about their jobs. Occasionally, he heard a laugh or good-natured ribbing. That was well. A crew that could laugh while working would faithfully execute their duties. He enjoyed the sameness of each day; it reassured him that his world remained rational. It had been two days since the Constellation had poked her nose into the Ross star system. Gallant congratulated himself on making the deployment from Earth so rapidly. It had been a long and arduous two-month grind, but Task Force 34 was finally ready to relieve Task Force 31 as guardian of this system. He shifted his mind back to the disturbing initial surveillance reports that had perplexed him for the last twenty-four hours. Task Force 31 was not visible, which by itself, wasn’t alarming. A planetary body might block their light, though they weren’t responding to radio signals either. Again, they might be on the other side of the star, and the speed of light wasn’t being accommodating. Another calculation percolated into his consciousness. He had sent Hawkeyes out on a sweep of the system. So far, nothing was amiss, but there was confusing radio chatter from the planets indicating that some horrific event had occurred recently. Gallant returned to the bridge in time to review the latest recon update. None of the information was reassuring. He noticed an anomaly in the data that prickled the hairs on the back of his neck. Though the statistics were mysteriously thin and precariously riddled with contaminated inconsistencies, they were coaxing him toward a disturbing conclusion. He worried his premonition might be correct and ordered the CIC to conduct an AI simulation analysis. It wasn’t long before Commander Fletcher stepped onto the bridge. “Good morning, Captain,” she said. Then with a frown, she added, “I have the results.” Gallant spun in his command chair and cast a concerned eye on her. She held a tablet by two fingers out in front of her as if she had found it in a vat of something vile. “Morning XO,” said Gallant, taking the device. Swiping through the screens, he absorbed the information while his heartbeat rose. He wanted to remain calm to reinforce his reputation as imperturbable. He didn’t want Fletcher or anyone else to suspect that he could lose his composure. But he was bursting to rush into CIC. He wanted to review the raw data to verify that it was accurate, but he knew that the analysts would have been meticulous in developing this report. She interrupted his concentration. “You were right, sir.” “Ha—h’m,” he said, clearing his throat. He took a deep breath and forced himself to appear relaxed. Fletcher shook her head and prodded, “Looks like an enormous debris field—possibly with escape pods.” She pointed to the area spread deep throughout the star system’s heart, halfway between planets Bravo and Charlie. The OOD and the chief of the watch inched closer, craning their necks to get a peek at the tablet. Gallant recalled the disturbing image of the original data. Understanding flooded over him. He visualized what must have taken place, and it took an enormous effort to suppress his emotions. She scowled. “No sign of Task Force 31.” Still, he didn’t respond. She muttered, “That doesn’t necessarily mean . . .” Everyone on the bridge gazed expectantly at him. Like a father who returns home to find his front door smashed open, he ordered, “OOD, open a channel to all ships.” A moment later, the OOD reported, “Channel open to all ships, Commodore.” “To all ships, this is Commodore Gallant; set general quarters, assume formation diamond 4.4.” “Aye aye, sir,” came the response from each ship. The task force split into four strike forces. Captain Jackson of the Courageous led the first strike force designated 34.1. It was followed one light hour behind by 34.2 and 34.3, led by Captain Hernandez of the Indefatigable and Captain Chu of the Inflexible, respectively. They kept a light-hour separation from each other. Finally, Gallant led Constellation and Invincible in 34.4, another light hour behind the rest. The cruisers and destroyers were split amongst the strike forces. The dispersed strike forces looked like a baseball diamond with the Constellation at home plate. It took several hours to complete the maneuver. Satisfied that the ships were sufficiently far apart for the majority to survive a blast from the Great Ship’s super-laser, he ordered, “Task Force change course to 030 Mark 2, all ahead full.” Gallant waited anxiously on the bridge for the entire twenty-four hours it took for the task force to crawl across the Ross star system. Some telltale blips appeared on the scope interspersed within a belt of asteroids. When they were finally close enough, they saw the remains of many half-dead ships. They began picking up distress signals of countless escape pods. Officers and watch-standers on the bridge stared at the viewscreen, trying to glimpse the wreckage. Gallant’s eye estimated the number of blips. They could only be the remnants of Task Force 31. It was worse than he imagined—a terrible loss of life. “OOD, prepare med-techs. Send the search and rescue teams to recover the escape pod survivors.” The initial action report was sent by the senior surviving officer, Captain Raymond. It was sketchy. It couldn’t be called a ‘battle’ report since not a single ship of the task force had fired a shot. After a brief visit to Constellation’s sickbay, the officer reported to Gallant’s stateroom. Raymond was not quite fifty, but his balding head, sunken eyes, and beaked nose made him appear older. His long black mustache with grey flecks drooped, making him appear to frown. His uniform was in tatters, and he had several bandaged injuries that had been tended to by the ship’s surgeon. His thickset body was powerful, but he stood slumped over, pain etched across his face. “That’s the scorched wreck of my ship, the Dauntless,” said Captain Raymond, pointing to the viewscreen. The broken battlecruiser, along with the crippled remnants of four cruisers and a dozen destroyers, were all that was left of Commodore Pearson’s Task Force 31. “Commodore Pearson orders were to hold the system at all costs. Admiral Graves had assured him that the Great Ship would not appear. He was told that it would have to protect the Chameleon home planet in the Cygni star system against the Titans. At least that was President Neumann’s thinking after he found out that the Chameleon had only the one Great Ship left.” “The United Planets has been in negotiation with the aliens for over a year,” said Gallant. “Was there no progress?” There was anguish in Raymond’s voice. “None. And the Chameleon were angry.” He paused, dropping his gaze. “The governor told them to shove off, no deal was possible. After that ultimatum, things turned ugly.” Gallant frowned. “Take your time and start from the beginning.” Raymond’s words were clipped. “Task Force 31 had one carrier, four battlecruisers, and two cruiser-destroyer squadrons between planets Charlie and Bravo when the Great Ship appeared. They demanded that the United Planets evacuate the star system. Well, you know Pearson, no way that was happening. He sounded battle stations and ordered his ships to disperse to present a minimal target for the Chameleons.” When Raymond hesitated, Gallant prompted, “What happened next?” “The action was a disaster—a complete shock. The Chameleon looked at the dispersion as a threat and warned him to stand-down, withdraw, or surrender. After a few minutes, they fired.” He cast his eyes down. “The single blast was so devastating that it destroyed nearly all our ships. The blinding light and searing heat crippled my Dauntless and disintegrated most of the task force. The crippled remainders launched escape pods and waited for a follow-up salvo that, mercifully, never came. We hobbled out of the way. I sent a message to the governor on Charlie.” Raymond swallowed hard and furrowed his brow. “The governor’s response was to call it ‘an unfortunate turn of events.’” “I learned later that the Chameleon had threatened to make peace with the Titans if we didn’t yield the system. They must have since it gave them the freedom of action to leave their home world unprotected and deal with us.” He handed Gallant a flash drive. “This contains a plot of the action and the recordings of the communications between our ships and the governor. I’ve stuck my neck out to get this information on the record. You should collect and check the wreckage along with my observations.” “I understand. Some powerful men in the admiralty will be worried. I will describe the action in a detailed report to be sent to Earth,” said Gallant. He worried about how to keep Task Force 34 from suffering the same fate as their predecessor.

Excerpt from Captain Henry Gallant book within the HEry Gallant Saga. Captain Henry Gallant AMAZON Chapter 1 Streak Across the Sky Cold night air smacked Rob Ryan in the face as he stepped out of the Liftoff bar—a favorite haunt of pilots. He was still weaving his way through the parking terminal looking for his single-seat jet-flyer when a familiar face appeared at his elbow. Grabbing his arm, his friend said, “You shouldn’t fly. Let me give you a ride.” Ryan straightened to his full six-two height and shrugged off his friend’s hand. “I’m fine,” he said, swiping a lock of unkempt brown hair out of his eyes. “Don’t be pigheaded. There’s a difference between self-reliance and foolishness.” He pushed past his friend. “Nonsense. I fly better when I’m . . . mellow.” As he left his buddy behind, he noticed a young woman who had come out of the bar after him. He had spent the past hour eyeing this smokin’ hot redhead, but she had been with somebody. Now she was heading out on her own. She glanced at him and quickened her pace. A thought penetrated the fog in his mind. I’ll show her. At his Cobra 777 jet-flyer, he zipped up his pressure suit, buckled into the cockpit, and pulled on his AI neural interface—all the while imagining a wild take-off that would wow the redhead. He jockeyed his jet along the taxiway onto the runway. When the turbo launch kicked in, the black-and-chrome jet spewed a cloud of exhaust and dust across the strip. He jammed the throttle all the way in and gave a whoop of pure joy at the roar and explosive thrust of the machine. The exhilaration—a primitive, visceral feeling—increased by the second, along with his altitude and speed. His love of speed was only matched by his almost unhealthy fascination with flying machines—too fast was never fast enough. For a few seconds, his mind flashed back to his very first flight. The thrill only lasted a few minutes before the mini flyer spun out and crashed. Without a word, his father picked him up and sat him back down in the seat, restarting the engine with a wink and a grin. Clearest of all was the memory of his father’s approval as he took off again and soared higher and faster than before. Now he sliced through the crisp night air in a military jet that had his name engraved on the side. He ignited an extra thruster to drive the engine even hotter. Riding the rush of adrenaline, he pulled back on the stick to pull the nose up. Atmospheric flying was different than being in space, and for him, it had a sensual rhythm all its own. As he reached altitude, he pulled a tight loop and snapped the jet inverted, giving himself a bird’s-eye view of the ground below. But instead of reveling in admiration as expected, he found himself fighting for control against a powerful shockwave as a Scorpion 699 jet blew past him. The blast of its fuel exhaust was nothing compared to the indignation and shame that burned his face. It was the redhead. Damn. She’s good. His pulse raced as he became fully alert. Determined to pursue her, he angled the ship across air traffic lanes, breaking every safety regulation in the book. Instinctively his eyes scanned the horizon and the edges around him, watching for threats or other machines that might interfere with his trajectory. Pinwheeling in a high-G turn, he felt the crush of gravity against his chest, yet still, his hand on the throttle urged ever more speed from the machine. He lost track of the Scorpion in the clouds, and in mere seconds she maneuvered behind him. He tried to shake her using every evasive maneuver he had learned in his fighter training but couldn’t do it. His eyes roamed the sky, watching for potential dangers. The night sky was dark, but several landmarks lit up the ground below him. Earth’s capital, Melbourne, glowed with activity to the north; a mountain range stretched across the horizon 50 km to the west, and an airport lay to the south at the edge of the ocean. As he scanned the skyline, he noticed a radio-telescope antenna. Impulsively he dove toward it, the Scorpion on his tail. At the last moment, the redhead broke pursuit to avoid the antenna, but in a moment of reckless folly, Ryan crashed through the flimsy wire mesh, no more substantial to his Cobra than a wisp of cloud. “That’ll need a patch,” he chuckled. But once more, the Scorpion blew by him. He watched it roar away as if he were in slow motion. As the redhead curved back toward him for another pass, he gritted his teeth in frustration. With thrusters already at max burn, he punched the afterburner to create his own shock wave and turned head-on into her path. “Damn!” he screamed as the other ship twisted away. His golden rule for staying alive while flying was “never yield but always leave yourself an out.” Folly had made him reckless, and he knew his reflexes were sluggish, but he was pissed at himself for letting this pilot provoke him. Recovering his reason, he leveled off and threw down the skid flaps to reach a more reasonable speed. The jet took the torque and inertia strain, and the flashing red lights on his display turned yellow and then green. Despite his irritation, he allowed himself a faint smile when his AI read the Scorpion’s registration: Lorelei Steward. Good sense advised that he throttle back, but pride won out. Spotting the Scorpion silhouetted against a cloud, he jammed the throttle forward yet again. Finally, behind her, his smile broadened. She wouldn’t slip away this time. She pulled her jet into a violent oblique pop, rolled inverted until the nose pointed to the ground then returned to upright. He stuck with her, move for move. Abruptly she angled for the nearby mountain range. He chased her, low and fast, through a pass and down into a twisting canyon, rolling and pitching in a dizzying display of aerobatic skill. He kept close on her six until they blew out of the ravine. In a desperate ploy to shake him, she turned back toward Melbourne’s airspace and headed straight into a crowded flying highway. Ryan was so close behind that it took a few seconds before he realized her blunder. She had turned into an oncoming traffic lane. The cockpit warning lights lit up the cabin as Ryan dodged a stream of oncoming vehicles. Up ahead, Lorelei ducked under a passenger liner that swerved directly into his path. Time slowed to a crawl as he foresaw his fate—he could escape by pulling up—but that would force the crowded passenger liner to dive and crash into the ground. “Damn it all!” he yelled and dove—leaving the liner a clear path to safety. Through the neural interface, his AI shrieked, TOO LOW! PULL UP! TOO LOW! PULL UP! He used every bit of expertise he could muster to twist, turn, and wrestle his jet into a controlled descent. His vision narrowed as the lights of city and ships gave way to a line of unyielding rocks zooming toward him. In a blink, he ran out of time—and altitude. BRACE FOR IMPACT! The Cobra plowed a trough a hundred meters long across the desert floor. Ryan sat in the cockpit, stunned and disoriented amid the flames and wreckage until his lungs convulsed from the dense smoke. An acidic stench and the taste of jet fuel assailed his nose and throat, rousing him from his stupor. Fumbling to unbuckle the safety harness, he held his breath until he could release the hatch and climb out of his ruined machine. Shaking hands searched his body for broken bones. To his relief, he was intact . . . if he didn’t count the ringing in his ears and the blood that coursed down his face. The maxim from flight school ran through his mind: “Any landing you walk away from . . .” But as he limped away, his beloved Cobra burned into a twisted mound of molten metal, its nose buried in the dusty red ground. He shook his head at the wreck. “Captain Gallant is going to have my ass.”

Excerpt of short story collection book, All Androids Lie. All Androids Lie AMAZON THE GAME Kateryna said, “Hold still, Dear,” as she wiped the dirty smudge off the corner of Maria’s mouth. Maria asked, “Why is everyone so excited?” Kateryna said, “They’re scared of the loud noise.” “What is it?” “Fireworks. See the bright flashes exploding in the night sky,” said the girl’s mother. Maria nodded. “It’s the start of The Game,” lied her mother. “I told you all about it. Don’t you remember?” Maria shook her head, puzzled. “Everyone in the city plays, and there are terrific prizes.” Kateryna added, “What a pity you’re only four. You can’t play. I’m sooo sorry. You might have been great.” “What’s the game?” “It’s a big, big game of tag. Everyone in the city will run to escape. If you’re tagged, you lose. Everyone wants to win. It’s too bad you can’t play.” “Why can’t I play?” Kateryna said, “You’re only four. You’d get tired, cry, and make a fuss.” “I won’t. I won’t make a fuss.” “You would have been good at this game. The prizes are spectacular. Including that new doll, Laura, that you wanted so badly.” “If I win, I will get Laura?” “Yes, and lots more.” Outside, people were running and shouting. “There are candies, treats, games, and other toys for the winners. But you could never win. You would cry and quit.” “No, Mommy. I’ll be good. I want to play and win the prizes.” “I’m so sorry, Dear. The game is long and hard, and I don’t think you’re strong enough.” “Oh, Mommy, I really, really want to. I promise to be good.” Maria looked as if she was ready to throw a tantrum. “None of that, or you will lose immediately,” scolded her mother. “Please?” she asked with the most adoring smile. “Well, I don’t know,” said her mother. “There are many people who can tag you, and you must run away from all of them.” “I will. Please?” Kateryna looked appreciatively at her fair-haired daughter. The prekindergarten teacher told her that Maria was her star pupil because she was so advanced with her numbers and letters. She loved her toy piano and played well with the other children. Kateryna could see herself in the child, not just in the likeness of her face and features but in spirit and desire. Normally, a good-natured and happy-go-lucky sort of woman, she felt she could rise to any challenge. And now, she faced her fiercest test. “If I let you play, there can be no quitting. Do you agree? Pinky Swear?” “Yes! Yes! Pinky Swear,” said Maria jumping up and down. Static from the radio crackled behind them. The news announcer said, “This city has been a center for trade and manufacturing for key businesses along the Black Sea coast. But now its magnificent architecture and unique decor are being wiped off the face of the earth.” With steely determination, Kateryna suppressed her fears and shut the radio off. As the explosions drew near, she calmly said, “Let’s get ready! “Keep these documents safe,” Kateryna said, tucking the papers into Maria’s coat pocket. “They are the game tickets with your name. The rules of the game are strict. And you must reach the winning flag without being tagged. You must stay close to me and don’t talk to people. Do you understand?” “Yes.” “Whenever I say run, you run. Or else, the bad men will tag you.” Maria nodded. She put a scarf around Maria’s neck and buttoned up her coat. Then she pulled up the collar before being satisfied that she would be warm. “My gloves,” squeaked Maria. “Here they are.” As they left their apartment building and stepped out onto the street, they saw people leaving their houses in panic. “Are all these people playing the game?” “Yes. See how much fun they’re having. I told you it was a popular game. You must be tough to play. Are you tough?” “Yes. Mommy.” “Are you?” her mother asked with a raised brow. The skinny four-year-old put her hands on her hips, stood like a superhero with her chest out, and shouted, “I’m tough, and I mean it!” Fairly bursting with laughter, Kateryna said, “Okay, then. Let’s go,” Kateryna gripped the girl’s hand firmly and said, “This way.” As they hurried, there were loud explosions throughout the city. When they reached the train station, shells were bursting high above. “Gosh! Everything is happening so fast.” “Be patient, Dear.” They managed to squeeze onto a packed rail car, but the train was slow and made many erratic stops as if it were engaged in a game of dodgeball. Soon Maria complained, “The people are scary.” Kateryna touched the girl’s cheek and said, “Be brave. We’re on a great adventure. You must be bold.” But after two hours, Maria scowled and said, “I’m cold.” As Kateryna rearranged the girl’s scarf and coat, deep frown lines bit into her face threatening to become a permanent mask. She removed the girl’s gloves and rubbed the tiny hands. Then she planted a kiss on Maria’s rosy cheek. Maria pouted, “I’m hungry.” “Maria, you’re a troublesome thing.” Kateryna took a package out of her pocket and unwrapped a Kanapky sandwich for her. The girl took several bites and then looked disinterested in the rest. She sulked, “I’m thirsty.” “I don’t have any water,” said her exasperated mother. “But if you’re going to be a nasty girl, we will have to quit the game and go home immediately.” “Mommmm,” whined Maria. Nearby, a very old, cantankerous-looking woman, rumpled and wrinkled as a walnut, said, “Here, I have an extra.” She handed Maria a small water bottle. “Thank you. That’s generous of you,” said Kateryna with relief. After another hour, Maria pressed her face against the window, peering into the night as February’s frost crept along the windowpane, forming the jagged lines of an ice blossom. Suddenly, the train bounced and rocked. Pieces of steel and glass flew about. People screamed in pain. A bit of shrapnel cracked the skull of a nearby man. It made the sound of a champagne cork popping. THUNK! “Mommy, that man is bleeding.” “Shhh. It was an accident. He will be taken care of. We must keep moving.” They fled the train and the bombardment area. Kateryna gripped her daughter’s hand tightly and pulled her along as quickly as possible. When they reached a military checkpoint, a soldier told them it was safer to travel on the back roads. “He’s dressed like Daddy. Is Daddy playing too?” “Yes, Darling,” said Kateryna, holding back a tear. “I’m afraid he is.” “I’m scared, Mommy.” Gathering her courage, Kateryna said, “Don’t be frightened, Maria. Remember, it’s only a game. And we’re going to win. Just don’t let them tag you, okay.” “Huh ha.” In the early morning hours, the rosy glow of the sun kissed the horizon just as they reached the top of a hill. “Can we rest, Mommy? I’m tired.” “Not yet. See that bunker across the field? That’s the finish line. When we get there, we’ll win the prize.” “Oh good,” said Maria, perking up, but she could barely move. Kateryna picked her up and carried her. But after going only a hundred yards, Maria exclaimed, “Huh, oh. Mommy are those the bad men?” pointing to men with guns chasing them. Kateryna looked over her shoulder and said, “Yes, Maria. They are very bad men. Evil does not sleep; it waits for a chance to catch you. So, we must hurry.” She put Maria down and said, “See that bunker ahead. That’s the finish line. That’s where you turn in your ticket. Hold it fast to your chest.” Then she leaned closer and whispered, “I love you, Dearest,” though the sentiment seemed more like goodbye. “I love you too, Mommy,” said Maria clutching her ticket. The child’s words wrapped around Kateryna like a thick warm blanket. She yelled, “Run, Maria, run!” The noise from the blasts was terrific and the flashes of the overhead lights cast eerie shadows on their path. Cold breath steamed from their mouths as they huffed and puffed. Gripped by the full force of her worst fears, Kateryna yelled, “Run, Maria! Don’t look back! Run!” Maria ran with all the might and passion a four-year-old could muster. Finally, when she reached the bunker, a giant armor-clad soldier pulled her to safety. Maria jumped up and down and shouted over the din, “Did we win, Mommy? Did we win?” Then, suddenly, and loudly, Maria let out a cry that tore through the night. She sobbed unrelentingly, even as she stuttered out several snot-thick breaths. In the open field, just a dozen yards from the bunker, her mother lay face-down, sprawled out like a discarded rag doll.

Ab excerpt of the non-fiction book the Semantic Web Development. Semantic Web Services AMAZON Chapter 6.0 The Semantic Web In this chapter, we provide an introduction to the Semantic Web and discuss its background and potential. By laying out a road map for its likely development, we describe the essential stepping stones including: knowledge representation, inference, ontology, search and search engines. We also discuss several supporting semantic layers of the Markup Language Pyramid Resource Description Framework (RDF) a nd Web Ontology Language (OWL). In addition, we discuss using RDF and OWL for supporting software agents, Semantic Web Services, and semantic searches. Background Tim Berners-Lee invented the World Wide Web in 1989 and built the World Wide Web Consortium (W3C ) team in 1992 to develop, extend, and standardize the Web. But he didn’t stop there. He continued his research at MIT through Project Oxygen[1] and began conceptual development of the Semantic Web. The Semantic Web is intended to be a paradigm shift just as powerful as the original Web. The goal of the Semantic Web is to provide a machine-readable intelligence that would come from hyperlinked vocabularies that Web authors would use to explicitly define their words and concepts. The idea allows software agents to analyze the Web on our behalf, making smart inferences that go beyond the simple linguistic analyses performed by today's search engines. Why do we need such a system? Today, the data available within HTML Web pages is difficult to use on a large scale because there is no global schema. As a result, there is no system for publishing data in such a way to make it easily processed by machines. For example, just think of the data available on airplane schedules, baseball statistics, and consumer products. This information is presently available at numerous sites, but it is all in HTML format which means that using it has significant limitations. The Semantic Web will bring structure and defined content to the Web, creating an environment where software agents can carry out sophisticated tasks for users. The first steps in weaving the Semantic Web on top of the existing Web are already underway. In the near future, these developments will provide new functionality as machines become better able to "understand" and process the data. This presumes, however, that developers will annotate their Web data in advanced markup languages. To this point, the language-development process isn't finished. There is also ongoing debate about the logic and rules that will govern the complex syntax. The W3C is attempting to set new standards while leading a collaborative effort among scientists around the world. Berners-Lee has stated his vision that today’s Web Services in conjunction with developing the Semantic Web, should become interoperable. Skeptics, however, have called the Semantic Web a Utopian vision of academia. Some doubt it will take root within the commercial community. Despite these doubts, research and development projects are burgeoning throughout the world. And even though Semantic Web technologies are still developing, they have already shown tremendous potential in the areas of semantic groupware (see Chapter 13) and semantic search (see Chapter 15). Enough so, that the future of both the Semantic Web and Semantic Web Services (see Chapter 11) appears technically attractive. The Semantic Web The current Web is built on HTML, which describes how information is to be displayed and laid out on a Web page for humans to read. In effect, the Web has developed as a medium for humans without a focus on data that could be processed automatically. In addition, HTML is not capable of being directly exploited by information retrieval techniques. As a result, the Web is restricted to manual keyword searches. For example, if we want to buy a product over the Internet, we must sit at a computer and search for most popular online stores containing appropriate categories of products. We recognize that while computers are able to adeptly parse Web pages for layout and routine processing, they are unable to process the meaning of their content. XML may have enabled the exchange of data across the Web, but it says nothing about the meaning of that data. The Semantic Web will bring structure to the meaningful content of Web pages, where software agents roaming from page-to-page can readily carry out automated tasks. We can say that the Semantic Web will become the abstract representation of data on the Web. And that it will be constructed over the Resource Description Framework (RDF) (see Chapter 7) and Web Ontology Language (OWL) (see Chapter 8). These languages are being developed by the W3C, with participations from academic researchers and industrial partners. Data can be defined and linked using RDF and OWL so that there is more effective discovery, automation, integration, and reuse across different applications. These languages are conceptually richer than HTML and allow representation of the meaning and structure of content (interrelationships between concepts). This makes Web content understandable by software agents, opening the way to a whole new generation of technologies for information processing, retrieval, and analysis. Two important technologies for developing the Semantic Web are already in place: XML and RDF. XML lets everyone create their own tags. Scripts, or programs, can make use of these tags in sophisticated ways, but the script writer has to know how the page writer uses each tag. In short, XML allows users to add arbitrary structure to their documents, but says nothing about what the structure means. If a developer publishes data in XML on the Web, it doesn’t require much more effort to take the extra step and publish the data in RDF. By creating ontologies to describe data, intelligent applications won’t have to spend time translating various XML schemas. In a closed environment, Semantic Web specifications have already been used to accomplish many tasks, such as data interoperability for business-to-business (B2B) transactions. Many companies have expended resources to translate their internal data syntax for their partners. As everyone migrates towards RDF and ontologies, interoperability will become more flexible to new demands. Another example of applicability is that of digital asset management. Photography archives, digital music, and video are all applications that are looking to rely to a greater degree on metadata. The ability to see relationships between separate media resources as well as the composition of individual media resources is well served by increased metadata descriptions and enhanced vocabularies. The concept of metadata has been around for years and has been employed in many software applications. The push to adopt a common specification will be widely welcomed. For the Semantic Web to function, computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning. AI researchers have studied such systems and produced today’s Knowledge Representation (KR). KR is currently in a state comparable to that of hypertext before the advent of the Web. Knowledge representation contains the seeds of important applications, but to fully realize its potential, it must be linked into a comprehensive global system. The objective of the Semantic Web, therefore, is to provide a language that expresses both data and rules for reasoning as a Web-based knowledge representation. Adding logic to the Web means using rules to make inferences and choosing a course of action. A combination of mathematical and engineering issues complicates this task (see Chapter 9). The logic must be powerful enough to describe complex properties of objects, but not so powerful that agents can be tricked by a paradox. Intelligence Concepts The concept of Machine Intelligence (MI) is fundamental to the Semantic Web. Machine Intelligence is often referred to in conjunction with the terms Machine Learning, Computational Intelligence, Soft-Computing, and Artificial Intelligence. Although these terms are often used interchangeably, they are different branches of study. For example, Artificial Intelligence involves symbolic computation while Soft-Computing involves intensive numeric computation. We can identify the following sub-branches of Machine Intelligence that relate to the Semantic Web: Knowledge Acquisition and Representation. Agent Systems. Ontology. Although symbolic Artificial Intelligence is currently built and developed into Semantic Web data representation, there is no doubt that software tool vendors and software developers will incorporate the Soft-Computing paradigm as well. The benefit is creating adaptive software applications. This means that Soft-Computing applications may adapt to unforeseen input. Knowledge Acquisition is the extraction of knowledge from various sources, while Knowledge Representation is the expression of knowledge in computer-tractable form that is used to help software-agents perform. A Knowledge Representation language includes Language Syntax (describes configurations that can constitute sentences) and Semantics (determines the facts and meaning based upon the sentences). For the Semantic Web to function, computers must have access to structured collections of information. But, traditional knowledge-representation systems typically have been centralized, requiring everyone to share exactly the same definition of common concepts. As a result, central control is stifling, and increasing the size and scope of such a system rapidly becomes unmanageable. In an attempt to avoid problems, traditional knowledge-representation systems narrow their focus and use a limited set of rules for making inferences. These system limitations restrict the questions that can be asked reliably. XML and the RDF are important technologies for developing the Semantic Web; they provide languages that express both data and rules for reasoning about the data from a knowledge-representation system. The meaning is expressed by RDF, which encodes it in sets of triples, each triple acting as a sentence with a subject, predicate, and object. These triples can be written using XML tags. As a result, an RDF document makes assertions about specific things. Subject and object are each identified by a Universal Resource Identifier (URI), just as those used in a link on a Web page. The predicate is also identified by URIs, which enables anyone to define a new concept just by defining a URI for it somewhere on the Web. The triples of RDF form webs of information about related things. Because RDF uses URIs to encode this information in a document, the URIs ensure that concepts are not just words in a document, but are tied to a unique definition that everyone can find on the Web. Search Algorithms The basic technique of search (or state space search) refers to a broad class of methods that are encountered in many different AI applications; the technique is sometimes considered a universal problem-solving mechanism in AI. To solve a search problem, it is necessary to prescribe a set of possible or allowable states, a set of operators to change from one state to another, an initial state, a set of goal states, and additional information to help distinguish states according to their likeliness to lead to a target or goal state. The problem then becomes one of finding a sequence of operators leading from the initial state to one of the goal states. Search algorithms can range from brute force methods (which use no prior knowledge of the problem domain, and are sometimes referred to as blind searches) to knowledge-intensive heuristic searches that use knowledge to guide the search toward a more efficient path to the goal state (see Chapters 9 and 15). Search techniques include: Brute force Breadth-first Depth-first Depth-first iterative-deepening Bi-directional Heuristic Hill-climbing Best-first A* Beam Iterative-deepening-A* Brute force searches entail the systematic and complete search of the state space to identify and evaluate all possible paths from the initial state to the goal states. These searches can be breadth-first or depth-first. In a breadth-first search, each branch at each node in a search tree is evaluated, and the search works its way from the initial state to the final state considering all possibilities at each branch, a level at a time. In the depth-first search, a particular branch is followed all the way to a dead end (or to a successful goal state). Upon reaching the end of a path, the algorithm backs up and tries the next alternative path in a process called backtracking. The depth-first iterative-deepening algorithm is a variation of the depth-first technique in which the depth-first method is implemented with a gradually increasing limit on the depth. This allows a search to be completed with a reduced memory requirement, and improves the performance where the objective is to find the shortest path to the target state. The bi-directional search starts from both the initial and target states and performs a breadth-first search in both directions until a common state is found in the middle. The solution is found by combining the path from the initial state with the inverse of the path from the target state. These brute force methods are useful for relatively simple problems, but as the complexity of the problem rises, the number of states to be considered can become prohibitive. For this reason, heuristic approaches are more appropriate to complex search problems where prior knowledge can be used to direct the search. Heuristic approaches use knowledge of the domain to guide the choice of which nodes to expand next and thus avoid the need for a blind search of all possible states. The hill-climbing approach is the simplest heuristic search; this method works by always moving in the direction of the locally steepest ascent toward the goal state. The biggest drawback of this approach is that the local maximum is not always the global maximum and the algorithm can get stuck at a local maximum thus failing to achieve the best results. To overcome this drawback, the best-first approach maintains an open list of nodes that have been identified but not expanded. If a local maximum is encountered, the algorithm moves to the next best node from the open list for expansion. This approach, however, evaluates the next best node purely on the basis of its evaluation of ascent toward the goal without regard to the distance it lies from the initial state. The A* technique goes one step further by evaluating the overall path from the initial state to the goal using the path to the present node combined with the ascent rates to the potential successor nodes. This technique tries to find the optimal path to the goal. A variation on this approach is the beam search in which the open list of nodes is limited to retain only the best nodes, and thereby reduce the memory requirement for the search. The iterative-deepening-A* approach is a further variation in which depth-first searches are completed, a branch at a time, until some threshold measure is exceeded for the branch, at which time it is truncated and the search backtracks to the most recently generated node. A classic example of an AI-search application is computer chess. Over the years, computer chess-playing software has received considerable attention, and such programs are a commercial success for home PCs. In addition, most are aware of the highly visible contest between IBM’s Deep Blue Supercomputer and the reigning World Chess Champion, Garry Kasparov in May 1997. Millions of chess and computing fans observed this event in real-time where, in a dramatic sixth game victory, Deep Blue beat Kasparov. This was the first time a computer has won a match with a current world champion under tournament conditions. Computer chess programs generally make use of standardized opening sequences, and end game databases as a knowledge base to simplify these phases of the game. For the middle game, they examine large trees and perform deep searches with pruning to eliminate branches that are evaluated as clearly inferior and to select the most highly evaluated move. We will explore semantic search in more detail in Chapter 15. Thinking The goal of the Semantic Web is to provide a machine-readable intelligence. But, whether AI programs actually think is a relatively unimportant question, because whether or not "smart" programs "think," they are already becoming useful. Consider, for example, IBM’s Deep Blue. In May 1997, IBM's Deep Blue Supercomputer played a defining match with the reigning World Chess Champion, Garry Kasparov. This was the first time a computer had won a complete match against the world’s best human chess player. For almost 50 years, researchers in the field of AI had pursued just this milestone. Playing chess has long been considered an intellectual activity, requiring skill and intelligence of a specialized form. As a result, chess attracted AI researchers. The basic mechanism of Deep Blue is that the computer decides on a chess move by assessing all possible moves and responses. It can identify up to a depth of about 14 moves and value-rank the resulting game positions using an algorithm prepared in advance by a team of grand masters. Did Deep Blue demonstrate intelligence or was it merely an example of computational brute force? Our understanding of how the mind of a brilliant player like Kasparov works is limited. But indubitably, his "thought" process was something very different than Deep Blue’s. Arguably, Kasparov’s brain works through the operation of each of its billions of neurons carrying out hundreds of tiny operations per second, none of which, in isolation, demonstrates intelligence. One approach to AI is to implement methods using ideas of computer science and logic algebras. The algebra would establish the rules between functional relationships and sets of data structures. A fundamental set of instructions would allow operations including sequencing, branching and recursion within an accepted hierarchy. The preference of computer science has been to develop hierarchies that resolve recursive looping through logical methods. One of the great computer science controversies of the past five decades has been the role of GOTO-like statements. This has risen again in the context of Hyperlinking. Hyperlinking, like GOTO statements, can lead to unresolved conflict loops (see Chapter 12). Nevertheless, logic structures have always appealed to AI researchers as a natural entry point to demonstrate machine intelligence. An alternative to logic methods is to use introspection methods, which observe and mimic human brains and behavior. In particular, pattern recognition seems intimately related to a sequence of unique images with a special linkage relationship. While Introspection, or heuristics, is an unreliable way of determining how humans think, when they work, Introspective methods can form effective and useful AI. The success of Deep Blue and chess programming is important because it employs both logic and introspection AI methods. When the opinion is expressed that human grandmasters do not examine 200,000,000 move sequences per second, we should ask, “How do they know?'' The answer is usually that human grandmasters are not aware of searching this number of positions, or that they are aware of searching a smaller number of sequences. But then again, as individuals, we are generally unaware of what actually does go on in our minds. Much of the mental computation done by a chess player is invisible to both the player and to outside observers. Patterns in the position suggest what lines of play to look at, and the pattern recognition processes in the human mind seem to be invisible to that mind. However, the parts of the move tree that are examined are consciously accessible. Suppose most of the chess player’s skill actually comes from an ability to compare the current position against images of 10,000 positions already studied. (There is some evidence that this is at least partly true.) We would call selecting the best position (or image) among the 10,000, insightful. Still, if the unconscious human version yields intelligent results, and the explicit algorithmic Deep Blue version yields essentially the same results, then couldn’t the computer and its programming be called intelligent too? For now, the Web consists primarily of huge number of data nodes (containing texts, pictures, movies, sounds). The data nodes are connected through hyperlinks to form `hyper-networks' can collectively represent complex ideas and concepts above the level of the individual data. However, the Web does not currently perform many sophisticated tasks with this data. The Web merely stores and retrieves information even after considering some of the “intelligent applications” in use today (including intelligent agents, EIP, and Web Services). So far, the Web does not have some of the vital ingredients it needs, such as a global database scheme, a global error-correcting feedback mechanism, a logic layer protocol, or universally accepted knowledge bases with inference engines. As a result, we may say that the Web continues to grow and evolve, but it does not learn. If the jury is still out on defining the Web as intelligent, (and may be for some time) we can still consider ways to change the Web to give it the capabilities to improve and become more useful (see Chapter 9). Knowledge Representation and Inference An important element of AI is the principle that intelligent behavior can be achieved through processing of symbol structures representing increments of knowledge. This has given rise to the development of knowledge-representation languages that permit the representation and manipulation of knowledge to deduce new facts. Thus, knowledge-representation languages must have a well-defined syntax and semantics system, while supporting inference. First let’s define the fundamental terms “data,” “information,” and “knowledge.” An item of data is a fundamental element of an application. Data can be represented by population and labels. Information is an explicit association between data things. Associations are often functional in that they represent a function relating one set of things to another set of things. A rule is an explicit functional association from a set of information things to a resultant information thing. So, in this sense, a rule is knowledge. Knowledge-based systems contain knowledge as well as information and data. The information and data can be modeled and implemented in a database. Knowledge-engineering methodologies address design and maintenance knowledge, as well as the data and information. Logic is used as the formalism for programming languages and databases. It can also be used as a formalism to implement knowledge methodology. Any formalism that admits a declarative semantics and can be interpreted both as a programming language and database language is a knowledge language. Three well-established techniques have been used for knowledge representation and inference: frames and semantic networks, logic based approaches, and rule based systems. Frames and semantic networks also referred to as slot and filler structures, capture declarative information about related objects and concepts where there is a clear class hierarchy and where the principle of inheritance can be used to infer the characteristics of members of a subclass from those of the higher level class. The two forms of reasoning in this technique are matching (i.e., identification of objects having common properties), and property inheritance in which properties are inferred for a subclass. Because of limitations, frames and semantic networks are generally limited to representation and inference of relatively simple systems. Logic-based approaches use logical formulas to represent more complex relationships among objects and attributes. Such approaches have well-defined syntax, semantics and proof theory. When knowledge is represented with logic formulas, the formal power of a logical theorem proof can be applied to derive new knowledge. However, the approach is inflexible and requires great precision in stating the logical relationships. In some cases, common-sense inferences and conclusions cannot be derived, and the approach may be inefficient, especially when dealing with issues that result in large combinations of objects or concepts. Rule-based approaches are more flexible. They allow the representation of knowledge using sets of IF-THEN or other condition action rules. This approach is more procedural and less formal in its logic and as a result, reasoning can be controlled in a forward or backward chaining interpreter. In each of these approaches, the knowledge-representation component (i.e., problem-specific rules and facts) is separate from the problem-solving and inference procedures. Resource Description Framework (RDF) The Semantic Web is built on syntaxes which use the Universal Resource Identifier (URI) to represent data in triples-based structures using Resource Description Framework (RDF) (see Chapter 7). A URI is a Web identifier, such as "http:" or "ftp:.” The syntax of URIs is governed by the IETF, publishers of the general URI specification the W3C maintains a list of URI schemes . In an RDF document, assertions are made about particular things having properties with certain values. This structure turns out to be a natural way to describe the vast majority of the data processed by machines. Subject, predicate, and object are each identified by a URI. The RDF triplets form webs of information about related things. Because RDF uses URIs to encode this information in a document the URIs ensure that concepts are not just words in a document, but are tied to a unique definition. All the triples result in a directed graph whose nodes and arcs are all labeled with qualified URIs. The RDF model is very simple and uniform. The only vocabulary is URIs which allow the use of the same URI as a node and as an arc label. This makes self-reference and reification possible, just as in natural languages. This is appreciable in a user-oriented context (like the Web), but is difficult to cope with in knowledge-based systems and inference engines. Once information is in RDF form, data becomes easier to process. We illustrate an RDF document in Example 6-1. This piece of RDF basically says that a book has the title "e-Video: Producing Internet Video," and was written by "H. Peter Alesso." Example 6-1 Listing 6-1 Sample RDF /XML H. Peter Alesso e-Video: Producing Internet Video The benefit of RDF is that the information maps directly and unambiguously to a decentralized model that differentiates the semantics of the application from any additional syntax. In addition, XML Schema restricts the syntax of XML applications and using it in conjunction with RDF may be useful for creating some datatypes. The goal of RDF is to define a mechanism for describing resources that makes no assumptions about a particular application domain, nor defines the semantics of any application. RDF models may be used to address and reuse components (software engineering), to handle problems of schema evolution (database), and to represent knowledge (Artificial Intelligence). However, modeling metadata in a completely domain independent fashion is difficult to handle. How successful RDF will be in automating activities over the Web is an open question. However, if RDF could provide a standardized framework for most major Web sites and applications, it could bring significant improvements in automating Web-related activities and services (see Chapter 11). If some of the major sites on the Web incorporate semantic modeling through RDF, it could provide more sophisticated searching capabilities over these sites (see Chapter 15). We will return to a detailed presentation of RDF in Chapter 7. RDF Schema The first "layer" of the Semantic Web is the simple data-typing model called a schema. A schema is simply a document that defines another document. It is a master checklist or grammar definition. The RDF Schema was designed to be a simple data-typing model for RDF. Using RDF Schema, we can say that "Desktop" is a type of "Computer," and that "Computer" is a sub class of “Machine”. We can also create properties and classes, as well as, creating ranges and domains for properties. All of the terms for RDF Schema start with namespace http://www.w3.org/2000/01/rdf-schema# . The three most important RDF concepts are "Resource" (rdfs:Resource), "Class" (rdfs:Class), and "Property" (rdf:Property). These are all "classes," in that terms may belong to these classes. For example, all terms in RDF are types of resource. To declare that something is a "type" of something else, we just use the rdf:type property: rdfs:Resource rdf:type rdfs:Class . rdfs:Class rdf:type rdfs:Class . rdf:Property rdf:type rdfs:Class . rdf:type rdf:type rdf:Property . This means "Resource is a type of Class, Class is a type of Class, Property is a type of Class, and type is a type of Property." We will return to a detailed presentation of RDF Schema in Chapter 7. Ontology A program that wants to compare information across two databases has to know that two terms are being used to mean the same thing. Ideally, the program must have a way to discover common meanings for whatever databases it encounters. A solution to this problem is provided by the Semantic Web in the form of collections of information called ontologies. Artificial-intelligence and Web researchers use the term ontology for a document that defines the relations among terms. A typical ontology for the Web includes a taxonomy with a set of inference rules. Ontology and Taxonomy We can express an Ontology as: Ontology = < taxonomy, inference rules> And we can express a taxonomy as: Taxonomy = < {classes}, {relations}> The taxonomy defines classes of objects and relations among them. For example, an address may be defined as a type of location, and city codes may be defined to apply only to locations, and so on. Classes, subclasses, and relations among entities are important tools. We can express a large number of relations among entities by assigning properties to classes and allowing subclasses to inherit such properties. Inference rules in ontologies supply further power. An ontology may express the rule "If a city code is associated with a state code, and an address uses that city code, then that address has the associated state code." A program could then readily deduce, for instance, that an MIT address, being in Cambridge, must be in Massachusetts, which is in the U.S., and therefore should be formatted to U.S. standards. The computer doesn't actually "understand" this, but it can manipulate the terms in a meaningful way. The real power of the Semantic Web will be realized when people create many programs that collect Web content from diverse sources, process the information and exchange the results. The effectiveness of software agents will increase exponentially as more machine-readable Web content and automated services become available. The Semantic Web promotes this synergy — even agents that were not expressly designed to work together can transfer semantic data. The Semantic Web will provide the foundations and the framework to make such technologies more feasible. Web Ontology Language (OWL) In 2003, the W3C began final unification of the disparate ontology efforts into a standardizing ontology called the Web Ontology Language (OWL). OWL is a vocabulary extension of RDF. OWL is currently evolving into the semantic markup language for publishing and sharing ontologies on the World Wide Web. OWL facilitates greater machine readability of Web content than that supported by XML, RDF, and RDFS by providing additional vocabulary along with formal semantics. OWL comes in several flavors as three increasingly-expressive sublanguages: OWL Lite, OWL DL, and OWL Full. By offering three flavors, OWL hopes to attract a broad following. We will return to detailed presentation of OWL in Chapter 8. Inference A rule may describe a conclusion that one draws from a premise. A rule can be a statement processed by an engine or a machine that can make an inference from a given generic rule. The principle of "inference" derives new knowledge from knowledge that we already know. In a mathematical sense, querying is a form of inference and inference is one of the supporting principles of the Semantic Web. For two applications to talk together and process XML data, they require that the two parties must first agree on a common syntax for their documents. After reengineering their documents with new syntax, the exchange can happen. However, using the RDF/XML model, two parties may communicate with different syntax using the concept of equivalencies. For example, in RDF/XML we could say “car” and specify that it is equivalent to “automobile.” We can see how the system could scale. Merging databases becomes recording in RDF that "car" in one database is equivalent to "automobile" in a second database. Indeed, this is already possible with Semantic Web tools, such as a Python program called "Closed World Machine” or CWM. Unfortunately, great levels of inference can only be provided using "First Order Predicate Logic," FOPL languages, and OWL is not entirely a FOPL language. First-order Logic (FOL) is defined as a general-purpose representation language that is based on an ontological commitment to the existence of objects and relations. FOL makes it easy to state facts about categories, either by relating objects to the categories or by quantifying. For FOPL languages, a predicate is a feature of the language which can make a statement about something, or to attribute a property to that thing. Unlike propositional logics, in which specific propositional operators are identified and treated, predicate logic uses arbitrary names for predicates and relations which have no specific meaning until the logic is applied. Though predicates are one of the features which distinguish first-order predicate logic from propositional logic, these are really the extra structure necessary to permit the study of quantifiers. The two important features of natural languages whose logic is captured in the predicate calculus are the terms "every" and "some" and their synonyms. Analogues in formal logic are referred to as the universal and existential quantifiers. These features of language refer to one or more individuals or things, which are not propositions and therefore force some kind of analysis of the structure of "atomic" propositions. The simplest logic is classical or boolean, first-order logic. The "classical" or "boolean" signifies that propositions are either true or false. First-order logic permits reasoning about the propositional and also about quantification ("all" or "some"). An elementary example of the inference is as follows: A ll men are mortal. John is a man. The conclusion: John is mortal. Application of inference rules provides powerful logical deductions. With ontology pages on the Web, solutions to terminology problems begin to emerge. The definitions of terms and vocabularies or XML codes used on a Web page can be defined by pointers from a page to an ontology. Different ontologies need to provide equivalence relations (defining the same meaning for all vocabularies), otherwise there would be a conflict and confusion. Software Agents Many automated Web Services already exist without semantics, but other programs, such as agents have no way to locate one that will perform a specific function. This process, called service discovery, can happen only when there is a common language to describe a service in a way that lets other agents understand both the function offered and the way to take advantage of it. Services and agents can advertise their function by depositing descriptions in directories similar to the Yellow Pages. There are some low-level, service-discovery schemes which are currently available. The Semantic Web is more flexible by comparison. The consumer and producer agents can reach a shared understanding by exchanging ontologies which provide the vocabulary needed for discussion. Agents can even bootstrap new reasoning capabilities when they discover new ontologies. Semantics also make it easier to take advantage of a service that only partially matches a request. An intelligent agent is a computer system that is situated in some environment, that is capable of autonomous action and learning in its environment in order to meet its design objectives. Intelligent agents can have the following characteristics: reactivity — they perceive their environment, and respond, pro-active — they exhibit goal-directed behavior and social — they interact with other agents. Real-time intelligent agent technology offers a powerful Web tool. Agents are able to act without the intervention of humans or other systems: they have control both over their own internal state and over their behavior. In complexity domains, agents must be prepared for the possibility of failure. This situation is called non-deterministic. Normally, an agent will have a repertoire of actions available to it. This set of possible actions represents the agent’s capability to modify its environments. Similarly, the action "purchase a house" will fail if insufficient funds are available to do so. Actions therefore have pre-conditions associated with them, which define the possible situations in which they can be applied. The key problem facing an agent is that of deciding which of its actions it should perform to satisfy its design objectives. Agent architectures are really software architectures for decision-making systems that are embedded in an environment. The complexity of the decision-making process can be affected by a number of different environmental properties, such as: Accessible vs inaccessible. Deterministic vs non- deterministic. Episodic vs non-episodic. Static vs dynamic. Discrete vs continuous. The most complex general class of environment is inaccessible, non-deterministic, non-episodic, dynamic, and continuous. Trust and Proof The next step in the architecture of the Semantic Web is trust and proof. If one person says that x is blue, and another says that x is not blue, will the Semantic Web face logical contradiction? The answer is no, because applications on the Semantic Web generally depend upon context, and applications in the future will contain proof-checking mechanisms and digital signatures. Semantic Web Capabilities and Limitations The Semantic Web promises to make Web content machine understandable, allowing agents and applications to access a variety of heterogeneous resources, processing and integrating the content, and producing added value for the user. The Semantic Web aims to provide an extra machine understandable layer, which will considerably simplify programming and maintenance effort for knowledge-based Web Services. Current technology at research centers allow many of the functionalities the Semantic Web promises: software agents accessing and integrating content from distributed heterogeneous Web resources. However, these applications are really ad-hoc solutions using wrapper technology. A wrapper is a program that accesses an existing Website and extracts the needed information. Wrappers are screen scrapers in the sense that they parse the HTML source of a page, using heuristics to localize and extract the relevant information. Not surprisingly, wrappers have high construction and maintenance costs since much testing is needed to guarantee robust extraction and each time the Website changes, the wrapper has to be updated accordingly. The main power of Semantic Web languages is that anyone can create one, simply by publishing RDF triplets with URIs. We have already seen that RDF Schema and OWL are very powerful languages. One of the main challenges the Semantic Web community faces for the construction of innovative and knowledge-based Web Services is to reduce the programming effort while keeping the Web preparation task as small as possible. The Semantic Web’s success or failure will be determined by solving the following: • The availability of content. • Ontology availability, development, and evolution. • Scalability – Semantic Web content, storage, and search are scalable. • Multilinguality – information in several languages. • Visualization – Intuitive visualization of Semantic Web content. • Stability of Semantic Web languages. Conclusion In this chapter, we provided an introduction to the Semantic Web and discussed its background and potential. By laying out a roadmap for its likely development, we described the essential stepping stones including: knowledge representation, inference, ontology, and search. We also discussed several supporting semantic layers of the Markup Language Pyramid Resource Description Framework (RDF) and Web Ontology Language (OWL). In addition, we discussed using RDF and OWL for supporting software agents, Semantic Web Services, and semantic search. [1] MIT's Project Oxygen is developing technologies to enable pervasive, human-centered computing and information-technology services. Oxygen's user technologies include speech and vision technologies to enable communication with Oxygen as if interacting directly with another person, saving much time and effort. Automaton, individualized knowledge access, and collaboration technologies will be used to perform a wide variety of automated, cutting-edge tasks.

Science fiction writers engage in research and discovery to fill their imagination. Research AI HIVE I invite you to join my AI community. Come on a journey into the future of artificial intelligence. AIHIVE has the potential to revolutionize many aspects of our lives, from the way we work to the way we interact with the world around us. Here, we explore the latest advances in AI, discuss the technical and ethical implications of this technology, and share our thoughts on the future. We believe that AI has the potential to make the world a better place, and we are committed to using this ability to create a world where AI benefits all of humanity. Here are some of the things you can find on our website: Directory of leading AI companies News and analysis on AI software Discussions about AI business opportunities Tutorials on artificial intelligence tools AI experts in Silicon Valley Video Software Laboratory The entertainment industry has always been at the forefront of technological innovation, continually transforming the way we create and consume content. In recent years, Artificial Intelligence (AI) and Computer-Generated Imagery (CGI) have become the primary forces driving this change. These cutting-edge technologies are now dominating the video landscape, opening up new possibilities for creators and redefining the limits of storytelling. AI video innovations are changing in Silicon Valley. Small businesses are creating AI video software tools for interchanging text, audio, and video media.

excerpt from the suspense thriller drama book Dark Genius. Dark Genius AMAZON Time Off (Excerpt) The next morning, Lawrence gazed up at the impressive face of Mont Blanc. The chill air penetrated even his warm clothing. He resolutely tugged on his ski gloves, slung his MIT scarf around his neck, and hefted his freshly waxed skis to his shoulder—he was all set. Boots climbing across the snow, he headed for the gondola. He could see the tiny figures of skiers already skimming down the steep slopes above, and his pulse quickened. As the group shuffled toward the gondola, he nodded to several familiar faces, relieved to find neither Proust nor Maurice among them. He thought he’d seen Emma in line ahead of him and fidgeted through the whole ride, oblivious to the spectacular view that spread below him. When he reached the advanced level, he got off, pulled his goggles down, and stepped into his skis. He picked Emma out immediately, even under her goggles and sporty ski hat. “Hi,” he said with a big smile, glad they both had the morning free from meetings. “Hi,” she replied, moving to his side in one smooth fluid push. Several others said, “Hello.” He returned a nod and pulled his jacket tightly around him against the chill air. A veteran skier strolled past with weathered skin and disrupted hair. He wore a turned-down smirk that challenged all comers to prove their worth. These were all experienced skiers, dressed for warmth, and equipped with the best quality gear. The first pair left together, plunging down onto the black runs. Others quickly followed, separated enough to avoid interference. Finally, he and Emma were the only ones on the top of the world. They felt as though they had the mountain all to themselves. Lawrence breathed in the crisp Swiss mountain air. It felt different somehow—cleaner, freer, better. The temperature was 5 C. He said, “Wow, what a fantastic day! This is an amazing resort, and the snow looks perfect.” “Something tells me I’m going to like this place.” “Me too.” Emma tugged on his scarf, and with mischief in her eyes, dared him, “Race you to the bottom.” He started to ask, “What do I get if I win?” when he realized she was already ten yards ahead. Though not an expert, he was a good skier. He shoved his poles hard into the snow and leaned forward, propelling himself down the slope after her. The skis hissed smoothly on the packed powder as he pulled himself along with his poles. Picking up speed on the gradually steepening slope, he was still falling behind. Going over the first vertical drop with spine-chilling ease, he found his rhythm and felt the adrenaline rush of speed, snow, and slope. Concentrating on his own maneuvering, he couldn’t watch Emma but could tell he still wasn’t gaining on her. He leaned over his skis, pulled up his poles, and dropped into a tuck. Instantly his speed increased, and his skis drifted a little farther apart than good style dictated. His hips and knees swiveled left–right–left–right–left in smooth, sweeping micro-turns, shoulders barely moving. Still, Emma held her lead ahead of him. A cluster of trees loomed ahead. He shifted his weight to come around, the right edges of his skis, biting hard into the slope and swung past them cleanly. He straightened up and turned to avoid several rocky obstacles. He maneuvered through a series of flags on the run, carving an extended S in the snow. He was close behind Emma now and could see her looking back at him, her face alive with pleasure. He was delighted. He aimed his skis straight down the slope again and felt the joy of zooming down a 45-degree drop. The thrill of speed and mastery of the terrain far outweighed any concern of potential danger. As he followed the curve of the mountain to the left, he came upon another row of flags, black and red, fluttering in the wind. The slope suddenly rose up under him, his knees compressed, and at this speed, he felt the lift as he caught air. He gave a shout of pure glee. Emma was near, and she ran an S-turn through his track. The slope eased a bit, and he jammed his left pole into the snow for leverage, pushing his skis down hard. The snow sprayed out from the abrupt stop and hung, crystallized, for a moment in the still air as he looked across a shoulder of the mountain. It plunged down toward a grove of trees, black in the distance. Breathing hard, he glanced over his shoulder but couldn’t see Emma. A momentary concern flashed through his mind, but then he caught a glimpse of her through some trees to his left. He swung back downhill and zig-zagged through the mounds beneath the gondola cables, driving his poles in hard with each knee-pounding bump. With her more direct route, Emma was ahead of him again. He pushed harder, trying to catch up to her, his knees straining on each turn. Without warning, his right ski caught an edge. He flailed, struggling to regain control, skidded, and fell. Shaking himself off, he quickly regained his feet, gasping for breath, and wiped the snow off his face and goggles. He stamped his feet to make sure his bindings were still tight, then set off in pursuit of Emma once more. Gaining speed, he schussed across the undulating ground, his skis intertwining with Emma’s tracks. A row of bright-orange warning signs made him check his speed sharply. This run had taken him dangerously close to a ravine. Behind the crossed sticks he could see where the cliff dropped and didn’t stop to think how far down it went into nothingness. He carved another hard turn, angling his skis back toward the left, and raced for the tree line. Keep forward. Get your hands in front of you. Set shoulders downslope, keep knees, and hips loose. The wind buffeted him, a pounding wall of resistance against his increasing speed. The wild schuss was nearing an end. Pine and spruce trees rushed by him, blurring into an impenetrable wall. The sun glistened over the snow’s surface, a sharp stretch of rocks and ravines was marked by warning flags thrown into high relief. Dark shadows obscured the terrain, making the slopes more dangerous. He knew there were sheer drops on each flank of the run. He felt an absurd desire to kick off his skies and run. Instead, he kept his focus on the track ahead and ignored the folds in the landscape. Finally, he saw an opening through the trees that had hemmed him in. He veered more left and shot through it. As he straightened his course, Emma whizzed by him, so close that he felt a spray of snow. Is she really that good, or did she misjudge her position? Trees pressed against the uphill side as the run curved around the mountain’s flank, their branches brittle against the white cold of the sky. Lake Geneva, now spread out in a breathtaking panorama below them. The thermometer had dropped precipitously to -3 C, and flakes of snow began to prick Lawrence’s cheek. Speed seemed no longer possible against the cold resisting wind. As the slope leveled out to the end of the run, he saw Emma out of the corner of his eye, only a few yards and scant seconds behind him. He angled his skis to cross the finish line. As his momentum slowed, he suddenly felt exhausted. His head throbbed, and his muscles ached from a combination of exertion and dehydration. His joints ground and creaked. His fingers refused to release their grip on the poles. Every sense seemed to have turned against him, and he blinked hard, his breathing labored. With an effort, he pulled off his soaked gloves and unzipped his jacket, sweating heavily. Stabbing his poles into the ground, he groaned as he bent over to unlatch his skis. Luckily the bindings sprang open easily, and he straightened painfully. The snow was falling faster now. He hadn’t noticed before. He cradled his stiff hands to his chest like a drowning man trying to catch his breath. The bracing wind stung his cheeks, leaving a bittersweet icy red welt. He was spent. As he looked for Emma, he wondered . . . Did I win?

Excerpts from my books and projects to encourage engagement. Excerpts Writing Porfolio The Henry Gallant Saga Midshipman Henry Gallant in Space Lieutenant Henry Gallant Henry Gallant and the Warrior Commander Gallant Captain Henry Gallant Commandor Henry Gallant Henry Gallant and the Great Ship Rear Admiral Henry Gallant Midshipman Henry Gallant at the Academy Dramatic Novels Youngblood Dark Genius Captain Hawkins Short Stories All Androids Lie Computer Books Connections Thinking on the Web The Semantic Web The Intelligent Wireless Web E-Video Computer Apps Graphic Novels Screenp lay

Excerpt of fourth book in the Henry Gallant Saga, Commander Gallant. Commander Henry Gallant AMAZON Chapter 1 Methane Planet As the warp bubble collapsed, the Warrior popped out on the edge of the Gliese-581star system. The Warrior was Captain Henry Gallant’s first command, the culmination of everything he’d worked for since entering the academy. With a rocket-shaped hull over one hundred meters long, she boasted stealth technology, a sub-light antimatter engine, and an FTL dark-matter drive. Gallant gawked. “What an awesome sight.” The busy bridge crew stole their eyes away from their instrument panels long enough to gaze in amazement at the Titan civilization. The many ships traveling between planets were remarkable, but the energy readings of the densely populated planets were off the charts. From his command chair, Gallant focused on the home of his alien enemy. The star was an M-class red dwarf—smaller, cooler, and less massive than Sol, at about twenty light-years from Earth. “Sir,” said the astrogator, “we’re three light-days from the sun. Five planets are visible.” “It’s like the solar system,” marveled Midshipman Stedman, an eager but green officer. His slight build and round, boyish face often seemed to get lost amid the bustle of the more experienced crew. “If you don’t notice that the sun is ruby instead of amber and that there are only five planets,” chided Chief Howard. The oldest member of the crew, he was a seasoned veteran with a slight potbelly. He wore his immaculate uniform with pride. Every ribbon, insignia, and star on his left breast had a long and glorious story. He was only too glad to retell the stories—with appropriate embellishments—over a whiskey, preferably Jack Daniels. The astrogator said, “Only two planets are within the liquid methane zone. But some of the asteroids and moons may have been methane-formed.” “Wow, the system is full of mining colonies, military bases, and communications satellites. The spaceship traffic is amazing. There must be many thousands of ships,” said Stedman. The astrogator reported, “Scans on the second and third planets show billions of beings. The second planet has the greatest energy density. I’ll bet that’s their planet of origin.” “Quite likely,” acknowledged Gallant, his curiosity aroused. “An imposing presence, Skipper,” said Roberts. Young and garrulous, Roberts had steady nerves and sound professional judgment. He was of average height with brown hair, a lean, smooth face, and a sturdy body. Gallant had come to trust him as a stalwart friend—something one only discovers during a crisis. That moment had come several months earlier when Roberts put his career and his life on the line for Gallant. “It has one large moon,” added Chief Howard. Gliese-Beta was a majestic ringed planet wrapped in a dense hydrocarbon nitrogen-rich atmosphere. It was opaque to blue light but transparent to red and infrared. The red dwarf's infrared warmed the planet and made it habitable for methane lifeforms. Gliese-Gamma was similar. The astrogator continued, “The next two planets are gas giants with several moons. Gliese-Delta is composed of hydrogen and helium with volcanic methane moons, much like our Neptune. Gliese-Epsilon is a low-mass planet with a climate model like a runaway greenhouse effect—analogous to our Venus.” “That’s interesting,” said Roberts. Encouraged, the astrogator concluded his report, “The system includes a disk-shaped asteroid field.” The Warrior used its radars and telescopes to plot the planets’ orbits. The CIC team computed the course of nearby contacts. The Warrior’s emission spectrum was controlled in stealth mode. “What’s your assessment of their military strength?” asked Gallant. The CIC team listed the large and small warships, followed by planetary defenses. They added an estimate of the traffic flow. It was a long list. OOD said, “Here is the compiled reported, sir.” “Very well,” acknowledged Gallant as he scanned the tablet. The sub-light engines drove the ship onward into the heart of the system. Calculating their flight path, the astrogator reported, “We’ll reach the asteroids in about forty-eight hours, sir.” Gallant tapped the screen to call up the AI settings for plot control and touched the destination. He ordered a deep-space probe sent toward the largest asteroids. “It’ll take several hours to start transmitting, sir.” Gallant said, “Once we get a base established, we can go deeper into their system. I’m interested in seeing how their home planet differs from our solar system. We need to learn about their society and leadership structure.” Roberts asked, “Skipper, we’ve always called them Titans, but what do they call themselves?” Gallant said, “I can’t replicate their name in our language. As autistic savants, their communication is different from our speech. We’ll continue to call them Titans.” The CIC team reported, “Our initial assessment shows that Gliese-Beta has a diverse topology and climate. It’s ecologically rich with many species. Extensive methane oceans and landmasses have abundant soil and temperature conditions. It can support a wide variety of methane-breathing lifeforms.” Roberts said, “It’s so different from our water-rich Earth.” “Earth is mostly water,” said Gallant. “The oceans provide us with fish to eat, water vapor to fill our skies with clouds, rain to nurture our crops, and water for us to drink. Our metabolism and food cycle are water-based, and we ourselves are 97 percent water. For us, water is life.” “How does this methane world sustain the Titans?” asked Roberts Gallant said, “The temperature variations provide methane in all three phases: gas, liquid, and solid. Methane rivers freeze at high latitudes to form polar sheets. The methane cycle is a complex molecular soup. It is formed from reactions when the ultraviolet radiation from the sun strikes the methane. Their methane life forms are comparable to our oxygen-based life cycle. And just as methane is a poison to us, oxygen is toxic to them.” Roberts asked, “Are they autistic savants because of the methane-based chemistry?” “That’s one of the things we’re here to learn. Our first task is to establish a base,” said Gallant. “From that hideout, the Warrior can recharge her stealth battery and remain safe between operations.” Maintaining stealth mode, the Warrior approached the outer edge of the asteroid belt. She conducted a spiral search to map the interrelated defenses. The crew looked for an asteroid large enough to hide the ship. Gallant and the XO combed through the CIC data to check for potential locations. “How about here, sir?” asked one of the analysts, pointing to a cluster near the outer perimeter of the field. The asteroid belt included many asymmetrical rocky bodies. Three smaller clusters skirted the outer edge. Some asteroids were more than one kilometer wide. “Yes, that might do,” said Gallant. “It’s large enough to block radar detection and shield us from view while we’re recharging. We’ll call this base Alamo." Gallant ordered a two-man team to construct a relay station on the asteroid. He left one of the Warrior’s remote-controlled drones on the surface along with a supply depot. Once Alamo was established, he settled the Warrior into orbit behind the rocks to recharge her stealth batteries. The next day, they reconnoitered the fifth planet and discovered a communication junction box. Moving deeper into Titan territory, they caught a bird’s-eye view of the alien’s home planet. They saw several orbiting shipyards and space stations. The Warrior collected information about the Titan fleet and civilization. The bridge crew was surprised at the incredible infrastructure the aliens had developed. Operating in such a populated environment was a challenge, but the cloaking technology allowed the Warrior to remain undetected. What followed were busy days as the Warrior peered into the inner workings of the Titan system. The crew compiled detailed lists of warships and their deposition, as well as their refueling and patrolling pattern. They learned shipping traffic patterns, monitored the industrial capacity, and accumulated population statistics. There were over twenty billion inhabitants. The Titans had built their main military headquarters on the third planet. It had a layered defense with satellites, minefields, and overlapping fortresses. A display showed fluctuating energy emissions for their industry. After two weeks of collecting information, Roberts approached Gallant’s command chair. He asked, “Captain, can you give us your game plan going forward?” Gallant recalled Admiral Collingsworth’s orders detailing their hazardous mission. All of which required his stealth ship and crew to be at peak operational and battle readiness. He said, “Yes. It’s time to fill you in. We’ve collected a lot of info, but scouting isn’t our sole mission. I intend to do more. Much more.” The bridge crew leaned closer, eager to drink in every tidbit of juicy news. He said, “We are finally ready to engage in asymmetric warfare. We will penetrate the Titan communication network to learn about their military deployment.” He paused for dramatic effect as everyone drew in a deep breath. “And we will raid commercial shipping to throw their civilian population into turmoil.” A buzz of excitement filled the bridge. “That’s a tall order, Skipper,” said Roberts. “Yes, it is.” Gallant asked, “Are you up for it?” “Can do, sir!” said Roberts. “Can do, sir!” roared the bridge crew. Commander Julie Ann McCall stepped out of CIC and onto the bridge. She walked straight to Gallant and grabbed his arm. She said, “I must speak to you immediately.” McCall was not a line officer. She was a product of genetic engineering who had inherited tendencies of the most diabolical kind, which made her a talented Solar Intelligence Agency (SIA) operative. Her considerable skills in manipulation and deception had fostered her brilliant career. What she lacked in kindness and empathy, she more than made up for in intellect, guile, and allure. She was astonishingly efficient at analyzing an opponent’s flaws. Some who had felt her cold-blooded sting labeled her a sociopath who would do anything to achieve her goals. Gallant’s long and checkered relationship with her remained a riddle to him. Now he gazed into her blazing eyes, and then he looked down at her hand on his arm. She pulled her hand away but repeated, “I need to speak with you privately.” The commotion on the bridge died down and the two senior officers became the focus of attention. Gallant rose from his command chair, and said, “Commander, please come with me.” All eyes on the bridge followed the pair as they left.

Excerpt from the sixth book in the Henry Gallant Saga, Commodore Henry Gallant. Commodore Henry Gallant AMAZON Chapter 1 Unidentified Flying Object Lieutenant Rob Ryan was bored. He hated the mundane tasks of being a squadron leader. He liked ‘fast’—the faster, the better. But that wasn’t happening today as he cruised over Earth in his Viper. He was stuck with the tedious job of training his new wingman, Glenn Holman, in strafing maneuvers against the Antarctic target range. As he executed a simple wingover in his starfighter, he wa s about to comment on the poor performance of his novice companion when out of nowhere, the world changed, shifting with shocking suddenness. That’s not possible! He instinctively flung an arm across his face to ward off the seemingly endless wall of steel that had materialized in front of him. I must be hallucinating! Heart-throbbing fear gripped him. But there is something delicious about fear. It starts with bitter panic and grows into sour excitement—until, at last, comes sweet courage. Ryan pulled his arm down, tightened his grip on the thruster, and yelled, “Hard to port! Max thrust! Flip gyros!” Over the next several seconds, he concentrated on avoiding a collision with the mountain of metal. In the first second, he felt the chest-crushing weight of 14 g’s as his Viper began the pivot. In the next second, he fought down the blackness of his vision, narrowing into a tunnel as 20 g’s tested the limitations of his pressure suit. By the third second, he felt as if he was being squashed like a ripe tomato—right before he blacked out. Several seconds later, he came to, blinking against the glare of the sun. Even as he aimed his ship toward it, he heard Holman gasp, “I can’t . . . make it . . .” Almost immediately, Ryan saw the brilliant red-white explosion of Holman’s Viper as it went splat against the steel wall. He sighed with relief when he saw an escape pod spiral toward Earth. *** The July blizzard howled across the high plateau of the Amundsen–Scott South Pole Antarctic Station, leaving a record snowfall of crystalline ice in its wake, and blustering so hard that the Earth defense sensor arrays were blanketed under the full fury of the whiteout. So powerful was the blizzard that sharp flecks of ice pierced the multilayered protective gear of the technician sent to investigate some minor static interference. As the man crawled toward the besieged sensors, his hands lost feeling despite the well-insulated flex-gloves. A large scavenger Skuas bird dive-bombed him, causing him to grab hold of the lifeline tether to keep from falling off the sheer rock cliff. “Damn!” “What’s wrong?” Against the howling of the wind, he could barely hear the question. During the six-month southern hemisphere ‘night,’ the wind blew at 160 km/h, and the temperature dropped to minus 89 °C. Despite the harsh conditions, the dry atmosphere and extended darkness made the station the Earth’s best location for astronomical observations. It had every conceivable type of sensor from microwave telescopes to neutrino detectors. The sensors were so accurate and dependable that the people of Earth rested reassured of their absolute safety. The man gripped the taut cable as he spoke into the mic, “Why do I always get the crap jobs?” “Just do it. And better hurry. Something big is brewing.” Inside the station’s geodesic dome, a sensor operator screamed, “Contact! Contact over Melbourne. It’s massive!” The duty officer came over to the operator’s station. “What’s the problem?” The operator pointed, his finger trembling in shock at the image that filled his screen. Flabbergasted, the duty officer asked, “Where did that come from? No unidentified contacts have been reported!” “It just popped up out of nowhere.” “That’s impossible.” “I’m telling you. Everything was normal, nothing but standard traffic patterns, and then WHAM! There it was.” “Have you run a diagnostic on your equipment?” “Look at the other sensors. They all show the same thing. We have a man outside checking some minor glitches, but nothing that would explain this.” “It isn’t a colossal malfunction? Do you think this is a bona fide contact?” “Yes, sir!” In the stunned silence, the senior chief operator said, “Designate contact as Tango 101, in geosynchronous orbit over Melbourne.” Still unable to grasp the situation, the duty officer asked again, “Why didn’t you spot this earlier?” “I’m telling you; it wasn’t there before. It came out of nowhere. As if it dropped out of cloak.” The dark eyes of the duty officer met the senior chief’s gaze. “That’s impossible. Even in a blizzard, our active sensors can penetrate any cloaking device within a million kilometers of Earth.” As he shook his head, the chief’s white hair fell across his grizzled face, but his eyes stayed steady. “Until now.” The officer asked, “What type of craft is it?” “Nothing in our databases even comes close. Visual images are starting to come in now. Man, it’s the strangest thing I’ve ever seen.” The officer’s eyes bugged out. “Oh, my Gawd! That’s incredible. It’s enormous. What the hell is it?” His hand smacked the red alert button, and his voice echoed over the base-wide intercom. “Activate planet defenses. Scramble standby fighters.” A second later, he said into the emergency radio, “Put me through to Admiral Devens, immediately.” When Admiral Devens responded, the duty officer said, “We have an unidentified flying object over the capital.” “Notify all missile and laser batteries to target the contact, but hold fire until further notice,” said the admiral, unruffled. “Have fighter command scramble all fighters and intercept the UFO.” *** “Fighter command, this is Lieutenant Ryan flying Constellation’s Viper 607. I have Tango 101 in sight.” Like a minnow swimming next to a blue whale, Ryan flew alongside the alien craft examining its features. He said, “Tango 101 is a monster ship that looks like a giant squid. It has an ellipsoid body thirty kilometers in diameter with protruding spikes seventy kilometers long. This Great Ship is beyond the combined resources of all the planets.” “Is it broadcasting?” asked the command center. “Negative, according to my sensors. It has not responded to radio communications, and I can detect no emissions at all, hostile or otherwise.” “Shadow it, but do not engage.” *** Twelve hours later, President Kent addressed the nation. “My fellow citizens, what you have heard is true. We have detected an alien vessel over Earth, but there is no immediate cause for alarm. Planetary defenses are on full alert. Our space fleet and fighters have surrounded the unknown spaceship. We do not know who these beings are, but they are not our Titan enemy. And though victory against that enemy may still seem a long way off, we are prepared to face any challenge they set against us. This new arrival has so far taken no hostile action, and our hope is that they will prove to be a benefactor rather than an adversary. “So, we must be patient until our visitor decides to speak. Until then, I am certain that you will all remain as brave and resolute as our proud space navy that stands guard protecting us at this moment.” Over the next several hours, news stations maintained uninterrupted coverage around the world. Opinions were divided over accepting the president’s optimism. Some listened to the vitriolic counterargument made by presidential candidate Gerome Neumann. He advised swift and total annihilation of the aliens who had violated Earth’s space. When it seemed that the tension couldn’t get any greater, an astounding event occurred. A shuttlecraft departed the Great Ship and landed at the Melbourne spaceport.

Excerpt of the book Midshipman Henry Gallant in Space. Midshipman Henry Gallant in Space AMAZON Joining the Fleet 1 A massive solar flare roared across the sun, crackling every display console in the tiny spacecraft. “No need to worry, young man. We’re almost there,” said the aged pilot. “I’m not concerned about the storm,” said newly commissioned Midshipman Henry Gallant. Eagerly, he shifted in his seat to get a better view of the massive battlecruiser Repulse that would be his home for the next two years. She was a magnificent fighting machine, a powerful beast in orbit around Jupiter. The pilot maneuvered to minimize the effects of the x-ray and gamma radiation until the craft slid into the cold black shadow of the Repulse. Gallant could hardly contain his delight as the tiny ship quivered in the grip of the warship’s tractors. By the time the docking hatch finally slid open, Gallant was waiting impatiently for his first glimpse inside the warship. He hurried to the bridge. The officer of the watch stood next to the empty captain’s chair, surrounded by a dizzying array of displays and virtual readouts. The officer rested his hand on the panel that concealed the Artificial Intelligence (AI) tactical analyzer. “Midshipman Henry Gallant, reporting aboard, sir.” Drawing his gangly seventeen-year-old figure to its full height, he gave a snappy salute. He tugged at his uniform jacket to pull the buttons into proper alignment. “Welcome aboard, Mr. Gallant. I’m Lieutenant Mather.” Mather was of average height, barrel-chested with angular facial features and a stoic look. Beyond a glance, he showed little interest in the new arrival. “Give me your comm pin.” Gallant handed over his pin, Mather made several quick selections on a touch screen, then swiped it past the chip reader. While his ID loaded into the ship’s computer, Gallant took the opportunity to look around. The semicircular compartment, though spacious, bristled with displays, control panels, and analysis stations. From his academy training, he could guess most of the functions. There were communications, radar, weapons, and astrogation, plus a few he couldn’t identify. Several of the positions were vacant operating automatically. Gallant’s fingers twitched, eager to be a part of the bridge’s efficient operation. A huge view screen dominating the compartment displayed Jupiter. An orbiting space station was visible against the vastness of the gas giant. He marveled at the spectacle. “Junior officer authorization verified. The ID pin has been updated with Repulse’s access codes,” a computer’s voice announced from a nearby speaker. Its neutral, disinterested tone reminded Gallant of a rather cold and distant teacher he had had in basic math years ago. ”Did you bring your gear aboard?” asked Mather. “My duffle bag is at the docking port, sir.” The aged pilot had helped Gallant carry his gear from the shuttlecraft onto Repulse. Then, after a cheery smile and a friendly, “Good luck,” he climbed back in his shuttle and left. Having no family of his own, Gallant had found some faint comfort in the good wishes. ”I’ll have your gear sent to your quarters. But, for now, you had better see the captain,” said Mather, raising an eyebrow at Gallant. “Aye aye, sir,” said Gallant. Mather turned to one of the bridge’s junior officers, a young woman. She wore a single thin gold stripe on her blouse sleeve, indicating her rank as Midshipman First Class, one-year senior to Gallant. He ordered, “Midshipman Mitchel, take Mr. Gallant to the captain’s cabin.” As they left the bridge, Mitchel said, “Henry Gallant . . . I remember you from the academy. I’m surprised you’re still in uniform.” Gallant gritted his teeth, as he had done many times before when confronted with what he perceived as overt disapproval. He didn’t recognize her, but he couldn’t help but observe that she was an attractive brunette with a trim figure. “Will you be training as a fighter pilot or missile weapons officer?” she asked. “I had basic fighter training on Mars and will be taking advanced pilot training with Repulse’s Squadron 111.” “I’m a qualified second-seat astrogator in 111. Most likely, we’ll wind up flying together at some point.” Because her demeanor revealed nothing about whether that idea repelled or appealed to her, Gallant nodded. When they reached the captain’s cabin, she said, “I’m Kelsey, by the way.” Then, as she turned to leave, she added as an afterthought, “Good luck.” Gallant watched her walk away. He wondered if her remark was sincere. *** Gallant stood like a statue inside the open hatch. Captain Kenneth Caine was seated with his back to him, reviewing Gallant’s military record, which was displayed on a computer screen. Clean-shaven with close-cropped graying hair, Caine was solidly built with square shoulders and a craggy face. His well-tailored uniform hugged his robust frame, accentuating his military bearing. From his brief time onboard, Gallant had already realized that Repulse was an orderly ship, and that Kenneth Caine was an orderly captain. Precision and discipline were expected. He was suddenly conscious that his tangled brown hair was longer than regulations allowed. The cabin was sparsely furnished in a traditional, starkly military fashion. A desk in one well-lit corner held the single personal item in the room: a photo of an attractive, mature woman with a pleasant smile. The sadness in her eyes hinted at the difficult bargain she had made as the lonely wife of a dedicated space officer. While the captain flipped through the personnel folder, Gallant’s gaze wandered to the compartment’s viewscreen. The solar flare had subsided, leaving gigantic colorful Jupiter filling most of the view. “At ease, Mr. Gallant,” said Caine, finally turning to face the newcomer. “Welcome aboard the Repulse.” Gallant relaxed his stance and said in a strong, clear voice, “Thank you, sir.” Caine looked him up and down and scrunched his face before asking, “What do you know of this ship’s mission, Mr. Gallant?” “As the flagship of the Jupiter Fleet, Repulse must prevent alien encroachment along the frontier, sir,” ventured Gallant. “Quite right, as far as that goes. But you’ll find, Mr. Gallant, that this task is more nuanced and layered than may be apparent. As a United Planets officer, you must find shades of meaning that can affect your performance. What would you surmise is behind this frontier watch?” The captain’s brisk voice demanded a resolute answer. Gallant spoke guardedly at first, but as his confidence grew, his voice gained assurance. “Well, sir, UP knows little about the aliens’ origins or intentions. They appear to have bases on the satellites of the outer planets. Clashes with their scout ships have proven troublesome, and Fleet Command wants to gather more intelligence. With so little known about alien technology, it isn’t easy to assess the best way to repel it. Still, this fleet must forestall an invasion of Earth by preventing the aliens from gaining a foothold in this sector.” ”And what would you say will be essential in achieving victory in battle?” Leaning forward with his hands behind him to balance out his jutting jaw, Gallant said with fierce intensity, “Surprise, sir! I assume that is why you’ve dispersed most of the fleet. So you can search the widest possible region of space for the first signs of significant alien activity.” Caine examined the young man again as if seeing him for the first time. “Good. We will not be the ones surprised. We will be prepared. You can appreciate how important it is that Repulse performs well.” Then, he added, “And I will allow nothing, and no one, to interfere with our mission.” “Yes, sir,” said Gallant, feeling the sting from the pointed comment. “Tell me, Mr. Gallant,” said the captain, shifting in his chair to find a more comfortable position, “why did you apply to the academy?” Gallant’s voice swelled with passion. “For as long as I can remember, I’ve wanted to pilot spaceships and explore the unknown, sir.” ”You are undoubtedly aware that many people wanted your hide raised up the flagpole.” Caine’s eyebrow twitched. “Although your progress for two academic years at the academy was respectable, many doubt that a Natural can compete in the fleet. Today, your real qualification for advancement is your double helix.” Caine continued, “Frankly, I’m astonished you have gotten this far without the advantages of genetic engineering. You’re a bit of a mystery that has yet to unfold.” Gallant didn’t like being referred to as a mystery, but he had his own uncertainty about how his future might evolve. Caine said, “Now that you are commissioned, you must serve a two-year deployment on Repulse. Then, if you complete all your qualifications and receive strong ranking marks, you may be recommended for promotion to ensign.” He gave a weak smile and added. “Learn your duties, obey orders, and you will have nothing to fear.” Caine searched Gallant’s face. “Well, nothing to say for yourself?” Gallant thrust his chin out and said, “I am prepared to do my duty to the best of my ability, sir!” “It is exactly ‘the best of your ability’ that is in question, young man,” responded Caine.

An excerpt from the non-fiction technology book Thinking on the Web. Thinking on the Web AMAZON Chapter 2 Gödel: What is Decidable? In the last chapter, we suggested that small wireless devices connected to an intelligent Web could produce ubiquitous computing and empower the Information Revolution. In the future, Semantic Web architecture is designed to add some intelligence to the Web through machine processing capabilities. For the Semantic Web to succeed the expressive power of the logic added to its mark-up languages must be balanced against the resulting computational complexit y. Therefore, it is important to evaluate both the expressive characteristics of logic languages, as well as, their inherit limitations. In fact, some options for Web logic include solutions that may not be solvable through rational argument. In particular, the work of Kurt Gödel identified the concept of undecidability where the truth or falsity of some statements may not be determined. In this chapter, we review some of the basic principles of logic and related them to the suitability for Web applications. First, we review the basic concept of logic, and discuss various characteristics and limitations of logic analysis. We introduce First Order Logics (FOL) and its subsets, such as Descriptive Logic and Horn Logic which offer attractive characteristics for Web applications. These languages set the parameters for how expressive Web markup languages can become. Second, we investigate how logic conflicts and limitations in computer programming and Artificial Intelligence (AI) have been handled in closed environments to date. We consider how errors in logic contribute to significant ‘bugs’ that lead to crashed computer programs. Third, we review how Web architecture is used to partition the delivery of business logic from the user interface. The Web architecture keeps the logic restricted to executable code residing on the server and delivers user-interface presentations residing within the markup languages traveling over the Web. The Semantic Web changes this partitioned arrangement. Finally, we discuss the implications of using logic in markup languages on the Semantic Web. Philosophical and Mathematical Logic Aristotle described man as a “rational animal” and established the study of logic beginning with the process of codifying syllogisms. A syllogism is a kind of argument in which there are three propositions, two of them premises, one a conclusion. Aristotle was the first to create a logic system which allowed predicates and subjects to be represented by letters or symbols. His logic form allowed one to substitute for subjects and predicates with letters (variables). For example: If A is predicated of all B, and B is predicated of all C, then A is predicated of all C. By predicated, Aristotle means B belongs to A, or all B's are A's. For instance, we can substitute subjects and predicates into this syllogism to get: If all humans (B's) are mortal (A), and all Greeks (C's) are humans (B's), then all Greeks (C's) are mortal (A). Today, Aristotle's system is mostly seen as of historical value. Subsequently, other philosophers and mathematicians such as Leibniz developed methods to represent logic and reasoning as a series of mechanical and symbolic tasks. They were followed by logicians who developed mechanical rules to carry out logical deductions. In logic, as in grammar, a subject is what we make an assertion about, and a predicate is what we assert about the subject. Today, logic is considered to be the primary reasoning mechanism for solving problems. Logic allows us to sets up systems and criteria for distinguishing acceptable arguments from unacceptable arguments. The structure of arguments is based upon formal relations between the newly produced assertions and the previous ones. Through argument we can then express inferences. Inferences are the processes where new assertions may be produced from existing ones. When relationships are independent of the assertions themselves we call them ‘formal’. Through these processes, logic provides a mechanism for the extension of knowledge. As a result, logic provides prescriptions for reasoning by machines, as well as, by people. Traditionally, logic has been studied as a branch of philosophy. However, since the mid-1800’s logic has been commonly studied as a branch of mathematics and more recently as a branch of computer science. The scope of logic can therefore be extended to include reasoning using probability and causality. In addition, logic includes the study of structures of fallacious arguments and paradoxes. By logic then, we mean the study and application of the principles of reasoning, and the relationships between statements, concepts or propositions. Logic incorporates both the methods of reasoning and the validity of the results. In common language, we refer to logic in several ways; logic can be considered as a framework or system of reasoning, a particular mode or process of reasoning, or the guiding principles of a field or discipline. We also use the term "logical" to describe a reasoned approach to solve a problem or get to a decision, as opposed to the alternative "emotional" approaches to react or respond to a situation. As logic has developed, its scope has splintered int o many distinctive branches. These distinctions serve to formalize different forms of logic as a science. The distinctions between the various branches of logic lead to their limitations and expressive capabilities which are central issues to designing the Semantic Web languages. The following sections identify some of the more important distinctions. Deductive and Inductive Reasoning Originally, logic consisted only of deductive reasoning which was concerned with a premise and a resultant deduction. However, it is important to note that inductive reasoning – the study of deriving a reliable generalization from observations – has also been included in the study of logic. Correspondingly, we must distinguish between deductive validity and inductive validity. The notion of deductive validity can be rigorously stated for systems of formal logic in terms of the well-understood notions of semantics. An inference is deductively valid if and only if there is no possible situation in which all the premises are true and the conclusion false. Inductive validity on the other hand requires us to define a reliable generalization of some set of observations. The task of providing this definition may be approached in various ways, some of which use mathematical models of probability. Paradox A paradox is an apparently true statement that seems to lead to a contradiction or to a situation that defies intuition. Typically, either the statements in question do not really imply the contradiction; or the puzzling result is not really a contradiction; or the premises themselves are not all really true (or, cannot all be true together). The recognition of ambiguities, equivocations, and unstated assumptions underlying known paradoxes has often led to significant advances in science, philosophy and mathematics. Formal and Informal Logic Formal logic (sometimes called ‘symbolic logic’) attempts to capture the nature of logical truth and inference in formal systems. This consists of a formal language, a set of rules of derivation (often called ‘rules of inference’), and sometimes a set of axioms. The formal language consists of a set of discrete symbols, a syntax (i.e., the rules for the construction of a statement), and a semantics (i.e., the relationship between symbols or groups of symbols and their meanings). Expressions in formal logic are often called ‘formulas.’ The rules of derivation and potential axioms then operate with the language to specify a set of theorems, which are formulas that are either basic axioms or true statements that are derivable using the axioms and rules of derivation. In the case of formal logic systems, the theorems are often interpretable as expressing logical truths (called tautologies). Formal logic encompasses a wide variety of logic systems. For instance, propositional logic and predicate logic are kinds of formal logic, as well as temporal logic, modal logic, Hoare logic and the calculus of constructions. Higher-order logics are logical systems based on a hierarchy of types. For example, Hoare logic is a formal system developed by the British computer scientist C. A. R. Hoare. The purpose of the system is to provide a set of logical rules in order to reason about the correctness of computer programs with the rigor of mathematical logic. The purpose of such a system is to provide a set of logical rules by which to reason about the correctness of computer programs with the rigor of mathematical logic. The central feature of Hoare logic is the Hoare triple. A triple describes how the execution of a piece of code changes the state of the computation. A Hoare triple is of the form: {P} C {Q} where P and Q are assertions and C is a command. P is called the precondition and Q the post-condition. Assertions are formulas in predicate logic. An interpretation of such a triple is: Whenever P holds of the state before the execution of C, then Q will hold afterwards. Alternatively, informal logic is the study of logic that is used in natural language arguments. Informal logic is complicated by the fact that it may be very hard to extract the formal logical structure embedded in an argument. Informal logic is also more difficult because the semantics of natural language assertions is much more complicated than the semantics of formal logical systems. Mathematical Logic Mathematical logic really refers to two distinct areas of research: the first is the application of the techniques of formal logic to mathematics and mathematical reasoning, and the second, the application of mathematical techniques to the representation and analysis of formal logic. The boldest attempt to apply logic to mathematics was pioneered by philosopher-logician Bertrand Russell. His idea was that mathematical theories were logical tautologies, and his program was to show this by means to a reduction of mathematics to logic. The various attempts to carry this out met with a series of failures, such as Russell's Paradox, and the defeat of Hilbert's Program by Gödel's incompleteness theorems (which we shall describe shortly). Russell's paradox represents either of two interrelated logical contradictions. The first is a contradiction arising in the logic of sets or classes. Some sets can be members of themselves, while others can not. The set of all sets is itself a set, and so it seems to be a member of itself. The null or empty set, however, must not be a member of itself. However, suppose that we can form a set of all sets that, like the null set, are not included in themselves. The paradox arises from asking the question of whether this set is a member of itself. It is, if and only if, it is not! The second form is a contradiction involving properties. Some properties seem to apply to themselves, while others do not. The property of being a property is itself a property, while the property of being a table is not, itself, a table. Hilbert's Program was developed in the early 1920s, by German mathematician David Hilbert. It called for a formalization of all of mathematics in axiomatic form, together with a proof that this axiomatization of mathematics is consistent. The consistency proof itself was to be carried out using only what Hilbert called ‘finitary’ methods. The special epistemological character of this type of reasoning yielded the required justification of classical mathematics. It was also a great influence on Kurt Gödel, whose work on the incompleteness theorems was motivated by Hilbert's Program. In spite of the fact that Gödel's work is generally taken to prove that Hilbert's Program cannot be carried out, Hilbert's Program has nevertheless continued to be influential in the philosophy of mathematics, and work on Revitalized Hilbert Programs has been central to the development of proof theory. Both the statement of Hilbert's Program and its refutation by Gödel depended upon their work establishing the second area of mathematical logic, the application of mathematics to logic in the form of proof theory. Despite the negative nature of Gödel's incompleteness theorems, a result in model theory can be understood as showing how close logics came to being true: every rigorously defined mathematical theory can be exactly captured by a First-Order Logical (FOL) theory. Thus it is apparent that the two areas of mathematical logic are complementary. Logic is extensively applied in the fields of artificial intelligence and computer science. These fields provide a rich source of problems in formal logic. In the 1950s and 60s, researchers predicted that when human knowledge could be expressed using logic with mathematical notation, it would be possible to create a machine that reasons, or produces artificial intelligence. This turned out to be more difficult than expected because of the complexity of human reasoning. In logic programming, a program consists of a set of axioms and rules. In symbolic logic and mathematical logic, proofs by humans can be computer-assisted. Using automated theorem proving, machines can find and check proofs, as well as work with proofs too lengthy to be written out by hand. However, the computation complexity of carrying out automated theorem proving is a serious limitation. It is a limitation that we will find in subsequent chapters significantly impacts the Semantic Web. Decidability In the 1930s, the mathematical logician, Kurt Gödel shook the world of mathematics when he established that, in certain important mathematical domains, there are problems that cannot be solved or propositions that cannot be proved, or disproved, and are therefore undecidable. Whether a certain statement of first order logic is provable as a theorem is one example; and whether a polynomial equation in several variables has integer solutions is another. While humans solve problems in these domains all the time, it is not certain that arbitrary problems in these domains can always be solved. This is relevant for artificial intelligence since it is important to establish the boundaries for a problem’s solution. Kurt Gödel Kurt Gödel (shown Figure 2-1) was born April 28, 1906 in Brünn, Austria-Hungary (now Brno, Czech Republic). He had rheumatic fever when he was six years old and his health became a chronic concern over his lifetime. Kurt entered the University of Vienna in 1923 where he was influenced by the lectures of Wilhelm Furtwängler. Furtwängler was an outstanding mathematician and teacher, but in addition he was paralyzed from the neck down, and this forced him to lecture from a wheel chair with an assistant to write on the board. This made a big impression on Gödel who was very conscious of his own health. As an undergraduate Gödel studied Russell's book Introduction to Mathematical Philosophy. He completed his doctoral dissertation under Hans Hahn in 1929. His thesis proved the completeness of the first order functional calculus. He subsequently became a member of the faculty of the University of Vienna, where he belonged to the school of logical positivism until 1938. Gödel is best known for his 1931 proof of the "Incompleteness Theorems." He proved fundamental results about axiomatic systems showing that in any axiomatic mathematical system there are propositions that cannot be proved or disproved within the axioms of the system. In particular, the consistency of the axioms cannot be proved. This ended a hundred years of attempts to establish axioms and axiom-based logic systems which would put the whole of mathematics on this basis. One major attempt had been by Bertrand Russell with Principia Mathematica (1910-13). Another was Hilbert's formalism which was dealt a severe blow by Gödel's results. The theorem did not destroy the fundamental idea of formalism, but it did demonstrate that any system would have to be more comprehensive than that envisaged by Hilbert. One consequence of Gödel's results implied that a computer can never be programmed to answer all mathematical questions. In 1935, Gödel proved important results on the consistency of the axiom of choice with the other axioms of set theory. He visited Göttingen in the summer of 1938, lecturing there on his set theory research and returned to Vienna to marry Adele Porkert in 1938. After settling in the United States, Gödel again produced work of the greatest importance. His “Consistency of the axiom of choice and of the generalized continuum-hypothesis with the axioms of set theory” (1940) is a classic of modern mathematics. In this he proved that if an axiomatic system of set theory of the type proposed by Russell and Whitehead in Principia Mathematica is consistent, then it will remain so when the axiom of choice and the generalized continuum-hypothesis are added to the system. This did not prove that these axioms were independent of the other axioms of set theory, but when this was finally established by Cohen in 1963 he used the ideas of Gödel. Gödel held a chair at Princeton from 1953 until his death in 1978. Propositional Logic Propositional logic (or calculus) is a branch of symbolic logic dealing with propositions as units and with the combinations and connectives that relate them. It can be defined as the branch of symbolic logic that deals with the relationships formed between propositions by connectives such as compounds and connectives shown below: Symbols Statement Connectives p q "either p is true, or q is true, or both" disjunction p · q "both p and q are true" conjunction p q "if p is true, then q is true" implication p q "p and q are either both true or both false" equivalence A ‘truth table’ is a complete list of the possible truth values of a statement. We use "T" to mean "true", and "F" to mean "false" (or "1" and "0" respectively). Truth tables are adequate to test validity, tautology, contradiction, contingency, consistency, and equivalence. This is important because truth tables are a mechanical application of the rules. Propositional calculus is a formal system for deduction whose atomic formulas are propositional variables. In propositional calculus, the language consists of propositional variables (or placeholders) and sentential operators (or connectives). A well-formed formula is any atomic formula or a formula built up from sentential operators. First-Order Logic (FOL) First-Order Logic (FOL), also known as first-order predicate calculus, is a systematic approach to logic based on the formulation of quantifiable statements such as "there exists an x such that..." or "for any x, it is the case that...”. A first-order logic theory is a logical system that can be derived from a set of axioms as an extension of first-order logic. FOL is distinguished from higher order logic in that the values "x" in the FOL statements are individual values and not properties. Even with this restriction, first-order logic is capable of formalizing all of set theory and most of mathematics. Its restriction to quantification of individual properties makes it difficult to use for the purposes of topology, but it is the classical logical theory underlying mathematics. The branch of mathematics called Model Theory is primarily concerned with connections between first order properties and first order structures. First order languages are by their nature very restrictive and as a result many questions can not be discussed using them. On the other hand first-order logics have precise grammars. Predicate calculus is quantificational and based on atomic formulas that are propositional functions and modal logic. In Predicate calculus, as in grammar, a subject is what we make an assertion about, and a predicate is what we assert about the subject. Automated Inference for FOL Automated inference using first-order logic is harder than using Propositional Logic because variables can take on potentially an infinite number of possible values from their domain. Hence there are potentially an infinite number of ways to apply the Universal-Elimination rule of inference. Godel's Completeness Theorem says that FOL is only semi-decidable. That is, if a sentence is true given a set of axioms, there is a procedure that will determine this. However, if the sentence is false, then there is no guarantee that a procedure will ever determine this. In other words, the procedure may never halt in this case. As a result, the Truth Table method of inference is not complete for FOL because the truth table size may be infinite. Natural deduction is complete for FOL, but is not practical for automated inference because the ‘branching factor’ in the search process is too large. This is the result of the necessity to try every inference rule in every possible way using the set of known sentences. Let us consider the rule of inference known as Modus Ponens (MP). Modus Ponens is a rule of inference pertaining to the IF/THEN operator. Modus Ponens states that if the antecedent of a conditional is true, then the consequent must also be true: (MP) Given the statements p and if p then q, infer q. The Generalized Modus Ponens (GMP) is not complete for FOL. However, Generalized Modus Ponens is complete for Knowledge Bases (KBs) containing only Horn clauses. An other very important logic that we shall discuss in detail in chapter 8 is Horn logic. A Horn clause is a sentence of the form: (Ax) (P1(x) ^ P2(x) ^ ... ^ Pn(x)) => Q(x) where there are 0 or more Pi's, and the Pi's and Q are positive (i.e., un-negated) literals. Horn clauses represent a subset of the set of sentences representable in FOL. For example: P(a) v Q(a) is a sentence in FOL, but is not a Horn clause. Natural deduction using GMP is complete for KBs containing only Horn clauses. Proofs start with the given axioms/premises in KB, deriving new sentences using GMP until the goal/query sentence is derived. This defines a forward chaining inference procedure because it moves "forward" from the KB to the goal. For example: KB = All cats like fish, cats eat everything they like, and Molly is a cat. In first-order logic then, (1) KB = (Ax) cat(x) => likes(x, Fish) (2) (Ax)(Ay) (cat(x) ^ likes(x,y)) => eats(x,y) (3) cat(Molly) Query: Does Molly eat fish? Proof: Use GMP with (1) and (3) to derive: (4) likes(Molly, Fish) Use GMP with (3), (4) and (2) to derive: eats(Molly, Fish) Conclusion: Yes, Molly eats fish. Description Logic Description Logics (DLs) allow specifying a terminological hierarchy using a restricted set of first-order formulas. DLs have nice computational properties (they are often decidable and tractable), but the inference services are restricted to classification and subsumption. That means, given formulae describing classes, the classifier associated with certain description logic will place them inside a hierarchy. Given an instance description, the classifier will determine the most specific classes to which the instance belongs. From a modeling point of view, Description Logics correspond to Predicate Logic statements with three variables suggesting that modeling is syntactically bound. Descriptive Logic is one possibility for Inference Engines for the Semantic Web. Another possibility is based on Horn-logic, which is another subset of First-Order Predicate logic (see Figure 2-2). In addition, Descriptive Logic and rule systems (e.g., Horn Logic) are somewhat orthogonal which means that they overlap, but one does not subsume the other. In other words, there are capabilities in Horn logic that are complementary to those available for Descriptive Logic. Both Descriptive Logic and Horn Logic are critical branches of logic that highlight essential limitations and expressive powers which are central issues to designing the Semantic Web languages. We will discuss them further in chapter 8. Using Full First-Order Logic (FFOL) for specifying axioms requires a full-fledged automated theorem prover. However, FOL is semi-decidable and doing inferencing becomes computationally untractable for large amounts of data and axioms. This means, than in an environment like the Web, FFOL programs will not scale to handle huge amounts of knowledge. Besides full first theorem proving would mean maintaining consistency throughout the Web, which is impossible. Description Logic fragment of FOL. FOL includes expressiveness beyond the overlap, notably: positive disjunctions; existentials; and entailment of non-ground and non-atomic conclusions. Horn FOL is another fragment of FOL. Horn Logic Program (LP) is a slight weakening of Horn FOL. "Weakening" here means that the conclusions from a given set of Horn premises that are entailed according to the Horn LP formalism are a subset of the conclusions entailed (from that same set of premises) according to the Horn FOL formalism. However, the set of ground atomic conclusions is the same in the Horn LP as in the Horn FOL. For most practical purposes (e.g., relational database query answering), Horn LP is thus essentially similar in its power to the Horn FOL. Horn LP is a fragment of both FOL and nonmonotonic LP. This discussion may seem esoteric, but it is precisely these types of issues that will decide both the design of the Semantic Web as well as is likelihood to succeed. Higher Order Logic Higher Order Logics (HOL's) provide greater expressive power than FOL, but they are even more difficult computationally. For example, in HOL's, one can have true statements that are not provable (see discussion of Gödel’s Incompleteness Theorem). There are two aspects of this issue: higher-order syntax and higher-order semantics. If a higher-order semantics is not needed (and this is often the case), a second-order logic can often be translated into a first-order logic. In first-order semantics, variables can only range over domains of individuals or over the names of predicates and functions, but not over sets as such. In higher-order syntax, variables are allowed to appear in places where normally predicate or function symbols appear. Predicate calculus is the primary example of logic where syntax and semantics are both first-order. There are logics that have higher-order syntax but first-order semantics. Under a higher-order semantics, an equation between predicate (or function) symbols, is true, if and only if logics with a higher-order semantics and higher-order syntax are statements expressing trust about other statements. To state it another way, higher-order logic is distinguished from first-order logic in several ways. The first is the scope of quantifiers; in first-order logic, it is forbidden to quantify over predicates. The second way in which higher-order logic differs from first-order logic is in the constructions that are allowed in the underlying type theory. A higher-order predicate is a predicate that takes one or more other predicates as arguments. In general, a higher-order predicate of order n takes one or more (n − 1)th-order predicates as arguments (where n > 1). Recursion theory Recursion is the process a procedure goes through when one of the steps of the procedure involves rerunning a complete set of identical steps. In mathematics and computer science, recursion is a particular way of specifying a class of objects with the help of a reference to other objects of the class: a recursive definition defines objects in terms of the already defined objects of the class. A recursive process is one in which objects are defined in terms of other objects of the same type. Using a recurrence relation, an entire class of objects can be built up from a few initial values and a small number of rules. The Fibonacci numbers (i.e., the infinite sequence of numbers starting 0, 1, 1, 2, 3, 5, 8, 13, …, where the next number in the sequence is defined a s the sum of the previous two numbers) is a commonly known recursive set. The following is a recursive definition of person's ancestors: One's parents are one's ancestors (base case). The parents of any ancestor are also ancestors of the person under consideration (recursion step). Therefore, your ancestors include: your parents, and your parents' parents (grandparents), and your grandparents' parents, and everyone else you get by successively adding ancestors. It is convenient to think that a recursive definition defines objects in terms of "previously defined" member of the class. While recursive definitions are useful and widespread in mathematics, care must be taken to avoid self-recursion, in which an object is defined in terms of itself, leading to an infinite nesting (see Figure 1-1: “The Print Gallery” by M.C. Escher is a visual illustration of self-recursion). Knowledge Representation Let’s define what we mean by the fundamental terms “data,” “information,” “knowledge,” and "understanding." An item of data is a fundamental element of an application. Data can be represented by populations and labels. Data is raw; it exists and has no significance beyond its existence. It can exist in any form, usable or not. It does not have meaning by itself. Information on the other hand is an explicit association between items of data. Associations represent a function relating one set of things to another set of things. Information can be considered to be data that has been given meaning by way of relational connections. This "meaning" can be useful, but does not have to be. A relational database creates information from the data stored within it. Knowledge can be considered to be an appropriate collection of information, such that it is useful. Knowledge-based systems contain knowledge as well as information and data. A rule is an explicit functional association from a set of information things to a specific information thing. As a result, a rule is knowledge. We can construct information from data and knowledge from information and finally produce understanding from knowledge. Understanding lies at the highest level. Understanding is an interpolative and probabilistic process that is cognitive and analytical. It is the process by which one can take existing knowledge and synthesize new knowledge. One who has understanding can pursue useful actions because he can synthesize new knowledge or information from what is previously known (and understood). Understanding can build upon currently held information, knowledge, and understanding itself. AI systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge. An important element of AI is the principle that intelligent behavior can be achieved through processing of symbolic structures representing increments of knowledge. This has produced knowledge-representation languages that allow the representation and manipulation of knowledge to deduce new facts from the existing knowledge. The knowledge-representation language must have a well-defined syntax and semantics system while supporting inference. Three techniques have been popular to express knowledge representation and inference: (1) Logic-based approaches, (2) Rule-based systems, and (3) Frames and semantic networks. Logic-based approaches use logical formulas to represent complex relationships. They require a well-defined syntax, semantics, and proof theory. The formal power of a logical theorem proof can be applied to knowledge to derive new knowledge. Logic is used as the formalism for programming languages and databases. It can also be used as a formalism to implement knowledge methodology. Any formalism that admits a declarative semantics and can be interpreted both as a programming language and a database language is a knowledge language. However, the approach is inflexible and requires great precision in stating the logical relationships. In some cases, common sense inferences and conclusions cannot be derived, and the approach may be inefficient, especially when dealing with issues that result in large combinations of objects or concepts. Rule-based approaches are more flexible and allow the representation of knowledge using sets of IF-THEN or other conditional rules. This approach is more procedural and less formal in its logic. As a result, reasoning can be controlled through a forward or backward chaining interpreter. Frames and semantic networks capture declarative information about related objects and concepts where there is a clear class hierarchy and where the principle of inheritance can be used to infer the characteristics of members of a subclass. The two forms of reasoning in this technique are matching (i.e., identification of objects having common properties), and property inheritance in which properties are inferred for a subclass. Frames and semantic networks are limited to representation and inference of relatively simple systems. In each of these approaches, the knowledge-representation component (i.e., problem-specific rules and facts) is separate from the problem-solving and inference procedures. For the Semantic Web to function, computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning. AI researchers have studied such systems and produced today’s Knowledge Representation (KR). KR is currently in a state comparable to that of hypertext before the advent of the Web. Knowledge representation contains the seeds of important applications, but to fully realize its potential, it must be linked into a comprehensive global system. Computational Logic Programming a computer involves creating a sequence of logical instructions that the computer will use to perform a wide variety of tasks. While it is possible to create programs directly in machine language, it is uncommon for programmers to work at this level because of the abstract nature of the instructions. It is better to write programs in a simple text file using a high-level programming language which can later be compiled into executable code. The ‘logic model’ for programming is a basic element that communicates the logic behind a program. A logic model can be a graphic representation of a program illustrating the logical relationships between program elements and the flow of calculation, data manipulation or decisions as the program executes its steps. Logic models typically use diagrams, flow sheets, or some other type of visual schematic to convey relationships between programmatic inputs, processes, and outcomes. Logic models attempt to show the links in a chain of reasoning about relationships to the desired goal. The desired goal is usually shown as the last link in the model. A logic program may consist of a set of axioms and a goal statement. The logic form can be a set of ‘IF-THEN’ statements. The rules of inference are applied to determine whether the axioms are sufficient to ensure the truth of the goal statement. The execution of a logic program corresponds to the construction of a proof of the goal statement from the axioms. In the logic programming model the programmer is responsible for specifying the basic logical relationships and does not specify the manner in which the inference rules are applied. Thus Logic + Control = Algorithms The operational semantics of logic programs correspond to logical inference. The declarative semantics of logic programs are derived from the term model. The denotation of semantics in logic programs are defined in terms of a function which assigns meaning to the program. There is a close relation between the axiomatic semantics of imperative programs and logic programs. The control portion of the equation is provided by an inference engine whose role is to derive theorems based on the set of axioms provided by the programmer. The inference engine uses the operations of resolution and unification to construct proofs. Faulty logic models occur when the essential problem has not been clearly stated or defined. Program developers work carefully to construct logic models to avoid logic conflicts, recursive loops, and paradoxes within their computer programs. As a result, programming logic should lead to executable code without paradox or conflict, if it is flawlessly produced. Nevertheless we know that ‘bugs’ or programming errors do occur, some of which are directly or indirectly a result of logic conflicts. As programs have grown in size from thousands of line of code to millions of lines, the problems of ‘bugs’ and logic conflicts have also grown. Today programs such as operating systems can have over 25 million lines of codes and considered to have hundreds of thousands of ‘bugs’ most of which are seldom encountered during routine program usage. Confining logic issues to beta-testing on local servers allows programmers reasonable control of conflict resolution. Now consider applying many lines of application code logic to the Semantic Web were it may access many information nodes. The magnitude of the potential conflicts could be somewhat daunting. Artificial Intelligence John McCarthy of MIT contributed the term ‘Artificial Intelligence’ (AI) and by the late 1950s, there were many researchers in AI working on programming computers. Eventually, AI expanded into such fields as philosophy, psychology and biology. AI is sometimes described in two ways: strong AI and weak AI. Strong AI asserts that computers can be made to think on a level equal to humans. Weak AI simply holds that some ‘thinking-like’ features can be added to computers to make them more useful tools. Examples of Weak AI abound: expert systems, drive-by-wire cars, smart browsers, and speech recognition software. These weak AI components may, when combined, begin to approach the expectations of strong AI. AI includes the study of computers that can perform cognitive tasks including: understanding natural language statements, recognizing visual patterns or scenes, diagnosing diseases or illnesses, solving mathematical problems, performing financial analyses, learning new procedures for problem solving, and playing complex games, like chess. We will provide a more detailed discussion on Artificial Intelligence on the Web and what is meant by machine intelligence in Chapter 3. Web Architecture and Business Logic So far we have explored the basic elements, characteristics, and limitations of logic and suggested that errors in logic contribute to many significant ‘bugs’ that lead to crashed computer programs. Next we will review how Web architecture is used to partition the delivery of business logic from the user interface. The Web architecture keeps the logic restricted to executable code residing on the server and delivering user interface presentations residing within the markup languages traveling along the Internet. This simple arrangement of segregating the complexity of logic to the executable programs residing on servers has minimized processing difficulties over the Web itself. Today, markup languages are not equipped with logic connectives. So all complex logic and detailed calculations must be carried out by specially compiled programs residing on Web servers where they are accessed by server page frameworks. The result is highly efficient application programs on the server must communicate very inefficiently with other proprietary applications using XML in simple ASCII text. In addition, there is difficulty in interoperable programming which greatly inhibits automation of Web Services. Browsers such as Internet Explorer and Netscape Navigator view Web pages written in HyperText Markup Language (HTML). The HTML program can be written to a simple text file that is recognized by the browser and it can call embedded script programming. In addition, HTML can include compiler directives that call server pages with access to proprietary compiled programming. As a result, simple-text HTML is empowered with important capabilities to call complex business logic programming residing on servers both in the frameworks of Microsoft’s .NET and Sun’s J2EE. These frameworks support Web Services and form a vital part of today’s Web. When a request comes into the Web server, the Web server simply passes the request to the program best able to handle it. The Web server doesn't provide any functionality beyond simply providing an environment in which the server-side program can execute and pass back the generated responses. The server-side program provides functions as transaction processing, database connectivity, and messaging. Business logic is concerned with logic about: how we model real world business objects - such as accounts, loans, travel; how these objects are stored; how these objects interact with each other - e.g. a bank account must have an owner and a bank holder's portfolio is the sum of his accounts; and who can access and update these objects. As an example, consider an online store that provides real-time pricing and availability information. The site will provide a form for you to choose a product. When you submit your query, the site performs a lookup and returns the results embedded within an HTML page. The site may implement this functionality in numerous ways. The Web server delegates the response generation to a script, however, the business logic for the pricing lookup is included from an application server. With that change, instead of the script knowing how to look up the data and formulate a response, the script can simply call the application server's lookup service. The script can then use the service's result when the script generates its HTML response. The application server serves the business logic for looking up a product's pricing information. That functionality doesn't say anything about display or how the client must use the information. Instead, the client and application server send data back and forth. When a client calls the application server's lookup service, the service simply looks up the information and returns it to the client. By separating the pricing logic from the HTML response-generating code, the pricing logic becomes reusable between applications. A second client, such as a cash register, could also call the same service as a clerk checking out a customer. Recently, eXtensible Markup Language (XML) Web Services use an XML payload to a Web server. The Web server can then process the data and respond much as application servers have in the past. XML has become the standard for data transfer of all types of applications. XML provides a data model that is supported by most data-handling tools and vendors. Structuring data as XML allows hierarchical, graph-based representations of the data to be presented to tools, which opens up a host of possibilities. The task of creating and deploying Web Services automatically requires interoperable standards. The most advanced vision for the next generation of Web Services is the development of Web Services over Semantic Web Architecture. The Semantic Web Now let’s consider using logic within markup languages on the Semantic Web. This means empowering the Web’s expressive capability, but at the expense of reducing Web performance. The current Web is built on HTML and XML, which describes how information is to be displayed and laid out on a Web page for humans to read. In addition, HTML is not capable of being directly exploited by information retrieval techniques. XML may have enabled the exchange of data across the Web, but it says nothing about the meaning of that data. In effect, the Web has developed as a medium for humans without a focus on data that could be processed automatically. As a result, computers are unable to automatically process the meaning of Web content. For machines to perform useful automatic reasoning tasks on these documents, the language machines use must go beyond the basic semantics of XML Schema. They will require an ontology language, logic connectives, and rule systems. By introducing these elements the Semantic Web is intended to be a paradigm shift just as powerful as the original Web. The Semantic Web will bring meaning to the content of Web pages, where software agents roaming from page-to-page can carry out automated tasks. The Semantic Web will be constructed over the Resource Description Framework (RDF) and Web Ontology Language (OWL). In addition, it will implement logic inference and rule systems. These languages are being developed by the W3C. Data can be defined and linked using RDF and OWL so that there is more effective discovery, automation, integration, and reuse across different applications. These languages are conceptually richer than HTML and allow representation of the meaning and structure of content (interrelationships between concepts). This makes Web content understandable by software agents, opening the way to a whole new generation of technologies for information processing, retrieval, and analysis. If a developer publishes data in XML on the Web, it doesn’t require much more effort to take the extra step and publish the data in RDF. By creating ontologies to describe data, intelligent applications won’t have to spend time translating various XML schemas. An ontology defines the terms used to describe and represent an area of knowledge. Although XML Schema is sufficient for exchanging data between parties who have agreed to the definitions beforehand, their lack of semantics prevents machines from reliably performing this task with new XML vocabularies. In addition, the ontology of RDF and RDF Schema (RDFS) is very limited (see Chapter 5). RDF is roughly limited to binary ground predicates and RDF Schema is roughly limited to a subclass hierarchy and a property hierarchy with domain and range definitions. Adding an Ontology language will permit the development of explicit, formal conceptualizations of models (see Chapter 6). The main requirements of an onotology language include: a well-defined syntax, a formal semantics, convenience of expression, n efficient reasoning support system, and sufficient expressive power. Since the W3C has established that the Semantic Web would require much more expressive power than using RDF and RDF Schema would offer, the W3C has defined Web Ontology Language (called OWL). The layered architecture of the Semantic Web would suggest that one way to develop the necessary ontology language is to extend RDF Schema by using the RDF meaning of classes and properties and adding primitives to support richer expressiveness. However, simply extending RDF Schema would fail to achieve the best combination of expressive power and efficient reasoning. The layered architecture of the Semantic Web promotes the downward compatibility and reuse of software is only achieved with OWL Full (see Chapter 6), but at the expense of computational intractability. RDF and OWL (DL and Lite, see Chapter 6) are specializations of predicate logic. They provide a syntax that fits well with Web languages. They also define reasonable subsets of logic that offer a trade-off between expressive power and computational complexity. Semantic Web research has developed from the traditions of Artificial Intelligence (AI) and ontology languages. Currently, the most important ontology languages on the Web are XML, XML Schema, RDF, RDF Schema, and OWL. Agents are pieces of software that work autonomously and proactively. In most cases agent will simply collect and organize information. Agents on the Semantic Web will receive some tasks to perform and seek information from Web resources, while communicating with other Web agents, in order to fulfill its task. Semantic Web agents will utilize metadata, ontologies, and logic to carry out its tasks. In a closed environment, Semantic Web specifications have already been used to accomplish many tasks, such as data interoperability for business-to-business (B2B) transactions. Many companies have expended resources to translate their internal data syntax for their partners. As the world migrates towards RDF and ontologies, interoperability will become more flexible to new demands. An inference is a process of using rules to manipulate knowledge to produce new knowledge. Adding logic to the Web means using rules to make inferences and choosing a course of action. The logic must be powerful enough to describe complex properties of objects, but not so powerful that agents can be tricked by a paradox. A combination of mathematical and engineering issues complicates this task. We will provide a more detailed presentation on paradoxes on the Web and what is solvable on the Web in the next few chapters. Inference Engines for the Semantic Web Inference engines process the knowledge available in the Semantic Web by deducing new knowledge from already specified knowledge. Higher Order Logic (HOL) based inference engines have to greatest expressive power among all known logics such as the characterization of transitive closure. However, higher order logics don't have nice computational properties. There are true statements, which are unprovable (Gödel’s Incompleteness Theorem). Full First Order Logic (FFOL) based inference engines for specifying axioms requires a full-fledged automated theorem prover. FOL is semi-decidable and doing inferencing is computationally not tractable for large amounts of data and axioms. This means, than in an environment like the Web, HOL and FFOL programs would not scale up for handling huge amounts of knowledge. Besides full first theorem proving would mean to maintain consistency throughout the web, which is impossible. Predicate calculus is the primary example of logic where syntax and semantics are both first-order. From a modeling point of view, Description Logics correspond to Predicate Logic statements with three variables suggesting that modeling is syntactically bound and is a good candidate language for Web logic. Other possibilities for inference engines for the Semantic Web are languages based on Horn-logic, which is another fragment of First-Order Predicate logic (see Figure 2-2). In addition, Descriptive Logic and rule systems (e.g., Horn Logic) have different capabilities. Both Descriptive Logic and Horn Logic are critical branches of logic that highlight essential limitations and expressive powers which are central issues to designing the Semantic Web languages. We will discuss them further in chapters, 6, 7, 8 and 9. Conclusion For the Semantic Web to provide machine processing capabilities, the logic expressive power of mark-up languages must be balanced against the resulting computational complexity of reasoning. In this chapter, we examined both the expressive characteristics of logic languages, as well as, their inherit limitations. First Order Logics (FOL) fragments such as Descriptive Logic and Horn Logic offer attractive characteristics for Web applications and set the parameters for how expressive Web markup languages can become. We also reviewed the concept of Artificial Intelligence (AI) and how logic is applied in computer programming. After exploring the basic elements, characteristics, and limitations of logic and suggesting that errors in logic contribute to many significant ‘bugs’ that lead to crashed computer programs, we reviewed how Web architecture is used to partition the delivery of business logic from the user interface. The Web architecture keeps the logic restricted to executable code residing on the server and delivering user interface presentations residing within the markup languages traveling along the Internet. Finally, we discussed the implications of using logic within markup languages on the Web through the development of the Semantic Web. Our conclusions from this chapter include: Logic is the foundation of knowledge representation which can be applied to AI in general and the World Wide Web specially. Logic can provide a high-level language for expressing knowledge and has high expressive power. Logic has a well-understood formal semantics for assigning unambiguous meaning to logic statements. In addition, we saw that proof systems exist that can automatically derive statements syntactically from premises. Predicate logic uniquely offers a sound and complete proof system while higher-order logics do not. By tracking the proof to reach its consequence the logic can provide explanations for the answers. Currently, complex logic and detailed calculations must be carried out by specially compiled programs residing on Web servers where they are accessed by server page frameworks. The result is highly efficient application programs on the server must communicate very inefficiently with other proprietary applications using XML in simple ASCII text. In addition, this difficulty for interoperable programs greatly inhibits automation of Web Services. The Semantic Web offers a way to use logic in the form of Descriptive Logic or Horn Logic on the Web. Exercises 2-1. Explain how logic for complex business calculations is currently carried out through .NET and J2EE application servers. 2-2. Explain the difference between FOL and HOL. 2-3. Why is it necessary to consider less powerful expressive languages for the Semantic Web? 2-4. Why is undeciability a concern on the Web? Website http://escherdroste.math.leidenuniv.nl/ offers visualize the mathematical structure behind Escher's Print Gallery using the Escher and the Droste effect. This mathematical structure answers some questions about Escher's picture, such as: "what's in the blurry white hole in the middle?" This project is an initiative of Hendrik Lenstra of the Universiteit Leiden and the University of California at Berkeley. Bart de Smit of the Universiteit Leiden runs the project. Interlude #2: Truth and Beauty As John passed with a sour look on his face, Mary looked up from her text book and asked, “Didn’t you enjoy the soccer game?” “How can you even ask that when we lost?” asked John gloomily. “I think the team performed beautifully, despite the score” said Mary. This instantly frustrated John and he said, "Do you know Mary that sometimes I find it disarming the way you express objects in terms of beauty. I find that simply accepting something on the basis of its beauty can lead to false conclusions?" Mary reflected upon this before offering a gambit of her own, "Well John, do you know that sometimes I find that relying on objective truth alone can lead to unattractive conclusions." John became flustered and reflected his dismay by demanding, "Give me an example." Without hesitation, Mary said, "Perhaps you will recall that in the late 1920's, mathematicians were quite certain that every well-posed mathematical question had to have a definite answer ─ either true or false. For example, suppose they claimed that every even number was the sum of two prime numbers,” referring to Goldbach's Conjecture which she had just been studying in her text book. Mary continued, “Mathematicians would seek the truth or falsity of the claim by examining a chain of logical reasoning that would lead in a finite number of steps to prove if the claim were either true or false." "So mathematicians thought at the time," said John. "Even today most people still do." "Indeed," said Mary. "But in 1931, logician Kurt Gödel proved that the mathematicians were wrong. He showed that every sufficiently expressive logical system must contain at least one statement that can be neither proved nor disproved following the logical rules of that system. Gödel proved that not every mathematical question has to have a yes or no answer. Even a simple question about numbers may be undecidable. In fact, Gödel proved that t here exist questions that while being undecidable by the rules of logical system can be seen to be actually true if we jump outside that system. But they cannot be proven to be true.” “Thank you for that clear explanation,” said John. “But isn’t such a fact simply a translation into mathematic terms of the famous Liar’s Paradox: ‘This statement is false.’” “Well, I think it's a little more complicated than that,” said Mary. “But Gödel did identify the problem of self-reference that occurs in the Liar’s Paradox. Nevertheless, Gödel’s theorem contradicted the thinking of most of the great mathematicians of his time. The result is that one can not be as certain as the mathematician had desired. See what I mean, Gödel may have found an important truth, but it was – well to be frank – rather disappointingly unattractive," concluded Mary. "On the contrary,” countered John, “from my perspective it was the beauty of the well-posed mathematical question offered by the mathematicians that was proven to be false. Mary replied, “I’ll have to think about that.”