Search Results

H. Peter Alesso wrote a self portrait to reveal his history and experiences that helped him on his writing journey. My Story I love words, but that wasn't always the case. I grew up with a talent for numbers, leading me to follow a different path. I went to Annapolis and MIT and became a nuclear physicist at Lawrence Livermore National Laboratory. Only after retiring was my desire to tell stories reawakened. In recent years, I have immersed myself in the world of words, drawing on my scientific knowledge and personal experience to shape my writing. As a scientist, I explored physics and technology, which enabled me to create informative and insightful books, sharing my knowledge with readers who sought to expand their understanding in these areas—contributing to their intellectual growth while satisfying my own passion. But it was my time as a naval officer, that genuinely ignited my imagination and propelled me into science fiction. After graduating from the United States Naval Academy and serving on nuclear submarines during both hot and cold wars, I witnessed firsthand the complexities and challenges of military operations that seamen face daily. This allowed me a unique perspective, which I channeled into creating Henry Gallant and a 22nd-century world where a space officer fought against invading aliens. Through this narrative, I explored the depths of human resilience, the mysteries of space, and the intricacies of military conflict. My stories let me share the highlights of my journey with you. I hope you enjoy the ride. 1/9 Contact First name* Last name Email* Write a message Submit

Excerpt from the eighth book in the Henry Gallant Saga, Rear Admiral Henry Gallant. Rear Admiral Henry Gallant AMAZON Chapter 1 Far Away Captain Henry Gallant was still far away, but he could already make out the bright blue marble of Earth floating in the black velvet ocean of space. His day was flat and dreary. Since entering the solar system, he had been unable to sleep. Instead, he found himself wandering around the bridge like a marble rattling in a jar. His mind had seemingly abandoned his body to meander on its own, leaving his empty shell to limp through his routine. He hoped tomorrow would bring something better. I’ll be home soon, he thought. A welcoming image of Alaina flashed into his mind, but it was instantly shattered by the memory of their last bitter argument. The quarrel had occurred the day he was deployed to the Ross star system and had haunted him throughout the mission. Now that incident loomed like a glaring threat to his homecoming. As he stared at the main viewscreen of the Constellation, he listened to the bridge crew’s chatter. “The sensor sweep is clear, sir,” reported an operator. Gallant was tempted to put a finger to his lips and hiss, “shh,” so he could resume his brooding silence. But that would be unfair to his crew. They were as exhausted and drained from the long demanding deployment as he was. They deserved better. He plopped down into his command chair and said, “Coffee.” The auto-server delivered a steaming cup to the armrest portal. After a few gulps, the coffee woke him from his zombie state. He checked the condition of his ship on a viewscreen. The Constellation was among the largest machines ever built by human beings. She was the queen of the task force, and her crew appreciated her sheer size and strength. She carried them through space with breathtaking majesty, possessing power and might and stealth that established her as the quintessential pride of human ingenuity. They knew every centimeter of her from the forward viewport to the aft exhaust port. Her dull grey titanium hull didn’t glitter or sparkle, but every craggy plate on her exterior was tingling with lethal purpose. She could fly conventionally at a blistering three-tenths the speed of light between planets. And between stars, she warped at faster than the speed of light. Even now, returning from the Ross star system with her depleted starfighters, battle damage, and exhausted crew, she could face any enemy by spitting out starfighters, missiles, lasers, and plasma death. After a moment, he switched the readout to scan the other ships in the task force. Without taking special notice, he considered the material state of one ship after another. Several were in a sorrowful dysfunctional condition, begging for a dockyard’s attention. He congratulated himself for having prepared a detailed refit schedule for when they reached the Moon’s shipyards. He hoped it would speed along the repair process. Earth’s moon would offer the beleaguered Task Force 34, the rest and restoration it deserved after its grueling operation. The Moon was the main hub of the United Planets’ fleet activities. The Luna bases were the most elaborate of all the space facilities in the Solar System. They performed ship overhauls and refits, as well as hundreds of new constructions. Luna’s main military base was named Armstrong Luna and was the home port of the 1st Fleet, fondly called the Home Fleet. Captain Julie Ann McCall caught Gallant’s eye as she rushed from the Combat Information Center onto the bridge. There was a troubled look on her face. Is she anxious to get home too? Was there someone special waiting for her? Or would she, once more, disappear into the recesses of the Solar Intelligence Agency? After all these years, she’s still a mystery to me. McCall approached him and leaned close to his face. In a hushed throaty voice, she whispered, “Captain, we’ve received an action message. You must read it immediately.” Her tight self-control usually obscured her emotions, but now something extraordinary appeared in her translucent blue eyes—fear! He placed his thumb over his command console ID recognition pad. A few swipes over the screen, and he saw the latest action message icon flashing red. He tapped the symbol, and it opened. TOP SECRET: ULTRA - WAR WARNING Date-time stamp: 06.11.2176.12:00 Authentication code: Alpha-Gamma 1916 To: All Solar System Commands From: Solar Intelligence Agency Subject: War Warning Diplomatic peace negotiations with the Titans have broken down. Repeat: Diplomatic peace negotiations with the Titans have broken down. What this portends is unknown, but all commands are to be on the highest alert in anticipation of the resumption of hostilities. Russell Rissa Director SIA TOP SECRET: ULTRA - WAR WARNING He reread the terse communication. As if emerging from a cocoon, Gallant brushed off his preoccupation over his forthcoming liberty. He considered the possibilities. Last month, he sent the sample Halo detection devices to Earth. He hoped that the SIA had analyzed the technology and distributed it to the fleet, though knowing government bureaucracy, he guessed that effort would need his prodding before the technology came into widespread use. Still, there should be time before it becomes urgent. The SIA had predicted that the Titans would need at least two years to rebuild their forces before they could become a threat again. Could he rely on that? Even though he was getting closer to Earth with every passing second, the light from the inner planets was several days old. Something could have already transpired. There was one immutable lesson in war: never underestimate your opponent. A shiver ran down his spine. This is bad. Very bad! Gone was the malaise that had haunted him earlier. Now, he emerged as a disciplined military strategist, intent on facing a major new challenge. Looking expectantly, he examined McCall’s face for an assessment. Shaking her head, she hesitated. “The picture is incomplete. I have little to offer.” Gallant needed her to be completely open and honest with him, but he was unsure how to win that kind of support. He rubbed his chin and spoke softly, “I’d like to tell you a story about a relationship I’ve had with a trusted colleague. And I’d like you to pretend that you were that colleague.” McCall furrowed her brow, but a curious gleam grew in her eyes. He said, “I’ve known this colleague long enough to know her character even though she has been secretive about her personal life and loyalties.” McCall inhaled and visibly relaxed as she exhaled. Her eyes focused their sharp acumen on Gallant. “She is bright enough to be helpful and wise enough not to be demanding,” continued Gallant. “She has offered insights into critical issues and made informed suggestions that have influenced me. She is astute and might know me better than I know myself because of the tests she has conducted. When I’ve strayed into the sensitive topic of genetic engineering, she has soothed my bumpy relationship with politicians.” He hesitated. Then added, “Yet, she has responsibilities and professional constraints on her candidness. She might be reluctant to speak openly on sensitive issues, particularly to me.” McCall’s face was a blank mask, revealing no trace of her inner response to his enticing words. He said, “If you can relate to this, I want you to consider that we are at a perilous moment. It is essential that you speak frankly to me about any insights you might have about this situation.” She swallowed and took a step closer to Gallant. Their faces were mere centimeters apart. “Very well,” she said. “The Chameleon are a spent force. After the loss of their last Great Ship, they are defenseless. They agreed to an unconditional surrender. They might even beg for our help from the Titans. Their moral system is like ours and should not be a concern in any forthcoming action. However, the Titans have an amoral empathy with other species.” He gave an encouraging nod. She added, “Despite the defeat of Admiral Zzey’s fleet in Ross, the Titans remain a considerable threat. They opened peace negotiations ostensibly to seek a treaty with a neutral zone between our two empires. But we can’t trust them. They are too aggressive and self-interested to keep any peace for long. One option they might try is to eliminate the Chameleon while they have the opportunity. Another is to rebuild their fleet for a future strike against us. However, the most alarming possibility would be an immediate attack against us with everything they currently have. They might even leave their home world exposed. But that would only make sense if they could achieve an immediate and overwhelming strategic victory.” Gallant grimaced as he absorbed her analysis. She concluded, “This dramatic rejection of diplomacy can only mean that they are ready to reignite the war—with a vengeance. They will strike us with swift and ruthless abandon.” Gallant turned his gaze toward the bright blue marble—still far away.

Excerpt from book 3 of the Henry Gallant Saga, Henry Gallant and the Warrior. Henry Gallant and the Warrior AMAZON Going Up 1 Lieutenant Henry Gallant plodded along the cobblestone streets of New Annapolis—head down, mind racing . . . My orders say take command of the Warrior immediately . . . but no promotion . . . Why not? He pondered the possibilities, but he already knew the answer. Though he had steely gray eyes, a square jaw, and was taller than nearly everyone around him, what distinguished him most was not visible to the naked eye—he was a Natural—born without genetic engineering. Is this my last chance to prove myself? By the time he reached the space elevator, the welcoming breeze of the clear brisk morning had brightened his mood and he fell into line behind the shipyard personnel without complaint. Looking up, he marveled: That cable climbs into the clouds like an Indian rope trick. When it was his turn at last, the guard scanned his comm pin against the access manifest. The portal light blinked red. “Pardon, sir. Access denied,” said the grim-faced sentry. “Call the officer of the guard,” demanded Gallant. The officer of the guard appeared but was no more inclined to pass Gallant through than the sentry was. The guard touched the interface panel and made several more entries, but the portal continued to blink red. “There’s a hold on your access, sir.” Trouble already? Gallant thought. Then he asked, “A hold?” “Yes, sir. Your clearance and authorization are in order, but SIA has placed a hold on your travel. They want you to report to SIA headquarters, A.S.A.P.” “I need to go to the shipyard and attend to important business before going to the Solar Intelligence Agency,” clarified Gallant, but even as he said it, he knew it wouldn’t help. “Sorry, sir. Orders.” Gallant noticed the four gold stripes of a captain’s sleeve. The officer was waiting to take the next elevator. “Captain?” he said, hailing the man before he recognized him. Captain Kenneth Caine of the Repulse marched to the guard post, frowning. “What can I do for you, Gallant?” Of all the luck, he thought. Caine was the last person he wanted to impose upon, but it was too late now. Several uncomfortable moments passed with the three of them standing there—Caine, Gallant, and the officer of the guard—staring at each other, waiting for someone to break the silence. Finally, Gallant addressed Caine: “Well, sir, I’ve received orders to take command of the Warrior, but apparently all the T’s haven’t been crossed and my shipyard access has a hold from SIA.” Caine’s frown deepened. Gallant turned to the officer of the guard and said, “Is it possible to allow me go to my ship and complete my business? I’ll report to SIA immediately afterward.” The officer of the guard fidgeted and squirmed. He understandably did not like being placed in such a position while under the scrutiny of a full captain. Caine shrugged. Gallant was puzzled for a moment, wondering how to win Caine’s support. He tried the officer of the guard again, “Perhaps, you could send a message to SIA headquarters stating that you informed me of my requirement to report and that I agreed to attend this afternoon after I assume command of my ship. I’ll initial it.” Caine nodded. The guard brightened visibly. “That should be acceptable, sir.” He made a few entries into his interface panel and the portal finally blinked green. Gallant stepped through the gate and joined Caine. Together they walked to the elevator doors and mingled with the group waiting for the next available car. “Thank you for your help, captain,” said Gallant. “I’m sorry to have troubled you.” Caine merely nodded. Unwilling to miss the opportunity to reconnect with his former commanding officer, Gallant asked, “How’ve you been, sir?” Caine’s frown returned. “Well, personally, it’s been quite a trial . . .” Gallant resisted the temptation to coax him onward. After a minute, Caine revealed, “I lost a lot of shipmates during the last action.” He sighed and took a moment to silently mourn their passing. “I’m sorry, sir,” said Gallant, who was sensitive to the prickling pain in Caine’s voice. Gallant then took a long look at the senior officer. He recalled a mental image of his former commanding officer—solidly built and squared shouldered with a crew-cut and a craggy face. In contrast, the man before him now was balding and flabby, with a puffy face and deep frown lines. “Humph,” grumbled Caine, recognizing Gallant’s critical stare. “You’ve changed too. You’re no longer the lanky callow midshipman who reported aboard the Repulse nearly five years ago.” “Thank you, sir,” said Gallant, breaking into an appreciative smile. Caine returned the smile and, warming to the conversation, he said, “We had a few good times back then—and a few victories as well—a good ship, a good crew.” A minute passed before Caine added, “As for the Repulse—she’s suffered along with her crew . . . perhaps more than her fair share. As you know, she’s has been in the forefront of battle since the beginning of the war, but when the Titans attacked Jupiter Station earlier this year, we took a terrible beating—along with the rest of the fleet.” Caine’s face went blank for a few seconds as he relived the event. “ The Titans used nuclear weapons to bombard the colonies. The loss of life was staggering. Jupiter’s moons are now lifeless, scorched rocks. The colonists fled on whatever transport they could find and they’re now in the refugee camp on the outskirts of this city,” said Caine. Then, trying to sound optimistic but unable to hide his concern, he added, “We gave the Titans some lumps as well. It’ll be some time before they can trouble us on this side of the asteroid belt.” “So I understand, sir.” SWOOSH! BAM! The elevator car doors opened with a loud bang. Caine stepped inside. Gallant grabbed the strap and buckled himself into the adjacent acceleration couch. A powerful engine pulled the glass-encased car along a ribbon cable anchored to the planet’s surface and extended to the Mars space station in geostationary orbit. A balance of forces kept the cable under tension while the elevator ascended—gravity at the lower end and the centripetal force of the station at the upper end. The tiny vehicle accelerated swiftly to seven g’s and reached orbit in less than ten minutes before braking to docking speed. Gallant enjoyed a spectacular view as the car sped through the clouds. Below him was the receding raw red and brown landscape of Mars spread over the planet’s curvature; above him was one of man’s most ambitious modern structures; —a space station, replete with a shipyard that housed the newest space vessels under construction including Gallant’s new command, the Warrior, as well as ships in need of repair, including the Repulse. Gallant tried to pick out his minute ship against the much larger battle cruisers nested near it, but the rotation of the station hid it from view. “Repulse is completing extensive repairs. She’ll be back in action before long. I have a fierce loyalty to my ship and I know she’ll acquit herself well, no matter what comes,” said Caine. “I’m sure she will, sir,” said Gallant. “I haven’t congratulated you on your first command, yet” Caine said, extending his hand. “You’ve earned it.” “Thank you, sir,” said Gallant, shaking hands, while a thought flashed through his mind, If I earned command, why wasn’t I promoted? “Do you have any idea of your first assignment, yet?” “No, sir. It could be almost anything,” said Gallant, but he was thinking, Probably involves the Warrior’s special capabilities. Caine said, “At least you’ll get a chance to strike the enemy.” Gallant said, “We still know so little about the aliens’ origins or intentions. Since they’ve taken Jupiter, they’ve expanded their bases from the satellites of the outer planets. They’ve also penetrated into the asteroids. That puts them in a position to launch raids here.” Caine said, “I once asked you, ‘What’s the single most important element in achieving victory in battle?’” “Yes, sir, and my answer is the same: surprise.” “Yes,” Caine said, “but to achieve surprise, it’s essential for us to gather more intelligence.” “I agree, sir.” “Tell me, Gallant,” Caine said, as he shifted position, “are you aware there are many people who hold you in contempt? They still doubt that a Natural can serve in the fleet.” Gallant grimaced. “I’ve always done my duty to the best of my ability, sir.” “And you have done admirably, from what I know of your actions, but that hasn’t fazed some. I’ve heard little about your last mission.” “I can’t discuss that mission, sir. It’s been classified as need-to-know under a special compartment classification,” said Gallant, as he thought, I wish I could tell you about the AI berserker machine. I can only imagine what’s in store for the Warrior. “Nevertheless, I’ve heard that Anton Neumann was much praised for that mission. He was promoted to full commander and given the cruiser Achilles, though, I wouldn’t be surprised if his father’s influence played a role in that.” Gallant said nothing, but stared down at his shoes, Neumann always wins. Caine grunted and then said, “Neither of us is in good standing with Anton’s father.” Caine and Gallant had previously run afoul of Gerome Neumann, President of NNR, Shipping and Mining Inc., and an industrial and government powerbroker. Gallant nodded. Upon arriving at the space station platform, the elevator car doors opened automatically and once again banged loudly. SWOOSH! BAM! A long, enclosed tunnel formed the central core of the station with twenty-four perpendicular arms that served as docking piers. The tunnel featured many windows and access ports to reach the twenty-four ships that extended from the docking arms. The two men chatted about the war news while they rode a tram along the tunnel causeway. Finally, Gallant left Caine at the Repulse and continued to his new command. A swarm of workmen buzzed along the Warrior’s scaffolding, cranes hauled machinery to and fro, and miscellaneous gear lay haphazardly about. An infinite amount of preparation was under way, servicing the ship in anticipation of her departure. Gallant gaped . . . There she is. He leaned forward to take in every line and aspect of the ship. Despite the distractions, he saw the ship as a thing of exquisite beauty. The Warrior featured a smooth rocket shaped hull and while she was smaller than her battle cruiser neighbors, she weighed thirty-thousand tons with an overall length of one hundred and twenty meters and a beam of forty meters. She was designed with stealth capability, so she emitted no detectable signals and remained invisible until her power supply required recharging. Her armament included a FASER cannon, several short-range plasma weapons, and several laser cannons. She was equipped with an armor belt and force shield plus electronic warfare decoys and sensors. The ship’s communications, navigation, FTL propulsion, and AI computer were all state-of-the-art. The crew of 126 officers and men, was highly trained and already on board. When the Warrior traveled through the unrelenting and unforgiving medium of space it would serve as the crew’s heartfelt home. The brief, relaxed sense of freedom that Gallant had enjoyed between deployments was coming to an end; his shoulders tightened in anticipation. He stepped onto the enclosed gangplank and saluted the flag that was displayed on the bow. Then he saluted the officer of the watch and asked, “Request permission to come aboard, sir?” “Permission granted, sir,” said Midshipman Gabriel in a gravelly voice that was totally at odds with his huge grin, dimpled cheeks, and boyish freckled face. Was I ever that young? thought Gallant before he recalled he was only a few years older. Boarding the ship, Gallant’s eyes widened as he sought to drink everything in. He was impressed by the innovative technologies that had been freshly installed. The novelty of his role on this ship was not lost on him. Upon reaching the bridge, he ordered Gabriel to use the ship’s intercom to call the crew to attention. “All officers, report to the bridge!” Gabriel ordered. When the officers had gathered around him a minute later, he said, “All hands, attention!” Drawn together on every deck, the crew stopped their work, came to attention, and listened. Gallant recited his orders, “Pursuant to fleet orders, I, Lieutenant Henry Gallant, assume command of the United Planet ship, Warrior, on this date at the Mars’ Space Station.” He continued reciting several more official paragraphs, but from that moment forward, the Warrior was a member of the United Planets’ fleet and Gallant was officially her commanding officer. With the formal requirements concluded, Gallant spoke over the address system: “At ease. Officers and crew of the Warrior, I’m proud to serve with you. I look forward to getting to know each one of you. For now, we must outfit this ship and prepare to do our job as part of the fleet. There are battles to be fought, a war to win, and the Warrior has a key role to play.” Satisfied with his brief statement, Gallant nodded to Gabriel. Over the address system Gabriel announced, “Attention! All hands dismissed! Return to your regular duties.” Gallant stood before the officers on the bridge, addressed each by name and shook their hands, starting with the executive officer and then the department heads; operations, engineering, and weapons; followed by the junior officers. His first impression was that they were an enthusiastic and professional group. “I will provide prioritized work items for each of you to address in the next few days as we prepare for our upcoming shakedown cruise. For now, you can return to your duties. Thank you.” Gallant entered the Combat Information Center and pulled on a neural interface to the ship’s AI. The dozens of delicate silicon probes touched his scalp at key points. It sensitively picked up wave patterns emanating from his thoughts and allowed him to communicate with the AI directly. Gallant formed a mental image of the Warrior's interior. While Gallant could use the interface for evaluating the ship’s condition, the controls remained under manual control. He hashed out his priorities for his department heads to work on and sent them messages. He ordered them to address the myriad of items he had been mentally considering for hours. While he would have liked to have had a discussion with each officer individually, that would simply have to wait. It was time to get back to the space elevator. Gallant frowned in frustration at being pulled away by his appointment: I’d better hustle to SIA.

Excerpt from the second book in the Henry Gallant Saga, Lieutenant Henry Gallant. Lieutenant Henry Gallant 1 RUN AMAZON Gallant ran—gasping for breath, heart pounding—the echo of his footsteps reverberated behind him. He hoped to reach the bridge, but hope is a fragile thing. Peering over his shoulder into the dark, he tripped on a protruding jagged beam, one of the ship’s many battle scars. As he crashed to the deck, the final glow of emergency lights sputtered out, leaving only the pitch black of power failure—his failure. He lay still and listened to the ship’s cries of pain; the incessant wheezing of atmosphere bleeding from the many tiny hull fissures, the repetitious groaning of metal from straining structures, and the crackling of electrical wires sparking against panels. Thoughts flashed past him. How long will the oxygen last? He was reluctant to guess. Where are they? The clamor of dogged footsteps drew closer even as he rasped for another breath. Trembling from exhaustion, he clawed at the bulkhead to pull himself up. His hemorrhaging leg made even standing brutally painful. Nevertheless, he ran. The bulkhead panels and compartment hatches were indistinguishable in the dimness. Vague phantoms lurked nearby even while his eyes adjusted to whatever glowing plasma blast embers flickered from the hull. As he twisted around a corner, he crashed his shoulder into a bulkhead. The impact knocked him back and spun him around. Reaching out with a bloody hand, he grasped the hatch handle leading into the Operation’s compartment. Going through the hatch, he pulled it shut behind him. He started to run, then awkwardly fought his own momentum, and stopped. Stupid! Stupid! Going back to the hatch, he hit the security locking mechanism. It wouldn’t stop a plasma blast, but it might slow them down, he thought. At least this compartment is airtight. Finally, able to take a deep breath, he tried to clear his head of bombarding sensations. He should’ve been in battle armor, but he’d stayed too long in engineering trying to maintain power while the hull had been breached and the ship boarded. Now his uniform was scorched, revealing the plasma burns of seared flesh from his left shoulder down across his back to his right thigh. He had no idea where the rest of the crew was; many were probably dead. His comm pin was mute, and the ship’s AI wasn’t responding. He had only a handgun, but, so far, he didn’t think they were tracking him specifically, merely penetrating into the ship to gain control. Gallant tried to run once more, but his legs were unwilling. Leaning against the bulkhead, like a dead weight, he slid slowly down to the deck. Unable to go farther, he sat dripping blood and trembling as the potent grip of shock grabbed hold. The harrowing pain of his burnt flesh swept over him. Hope and fear alike abandoned him, leaving only an undeniable truth; without immediate medical treatment, he wouldn’t survive. I’m done. Closing his eyes, he fought against the pain and the black vertigo of despair. He took a deep breath and called upon the last of his inner resolve and resilience . . . No! I won’t give up. Exhaling and opening his eyes, he caught sight of a nearly invisible luminescent glow of a Red Cross symbol, offering him a glimmer of hope. He stretched his arm toward the cabinet. “Argh.” He heard a cry of agony and only belatedly realized it had escaped his own lips as he strained to pull away twisted metal from the door to a medical cabinet. Reaching inside, he grabbed a damaged medi-pack. Painstakingly he used the meager emergency provisions to stop the bleeding and to infuse blood plasma. His limited mobility prevented him from reaching awkward areas, but he managed to insert an analgesic hypodermic into his raw, blistered flesh. Next, he crudely bandaged his suffering body. He relaxed momentarily as the medication coursed through his veins, working to stifle the worst effects of shock and blood loss. His parched throat demanded . . . Water. He looked at more cabinets but was unable to make out their markings in the dark. Stretching his fingers, he opened the nearest one, groping for something familiar inside. No. He opened the next. No. And another. Yes. Finally, he snatched a half-buried survival kit. Greedily he drank and even managed to take a few bites of an energy bar. A surge of adrenaline helped him shift his position to sit more comfortably as his mind came into sharper focus. As he examined his surroundings in the faint light, he spotted an interface station. He was about to reach up and patch into the ship’s AI to get an update on the ship’s defensive posture when he was disturbed by the dismal clangor of footsteps. He held his breath. Are they coming this way?

The Science Fiction series "The Henry Gallant Saga." The Henry Gallant Saga COURAGE is only a word . . . until you witness it. Then . . . it is contagious. Henry Gallant is the only Natural left in Earth's genetically engineered space navy. Despite overwhelming odds and the doubts of his shipmates, Gallant refuses to back down as he uses his unique abilities to fight for victory at the farthest reaches of the Solar System. Follow Gallant as he finds the spine to stand tall, vanquish fear, and rain violence upon the methane-breathing enemy aliens. The nation needs a hero like Henry Gallant. He fights! For fans of Horatio Hornblower and Honor Harrington.

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?