Beaches – Kissimmee Florida Vacations

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Relax. Feel the white sand between your toes. Listen to the splash of waves. Build castles close to the ocean's edge. Isn't that what you've been dreaming about for what seems like forever? You just know that the cool breeze gently caressing your skin while the water laps at your feet will wash away all that stress you've been carrying around. You're probably beginning to relax even now as you imagine sunsets along a broad horizon dotted with seagulls and billowing sails. You can have it all, just a short drive from your Kissimmee, Florida vacation destination.

A family or romantic seaside beach getaway is almost a must for a Florida vacation. Just an hour's trip from the Orlando area are some of the best east coast beaches of theAtlantic Ocean, the ideal setting for lounging, surfing or deep-sea fishing. To the west, theGulf Coastis about a 90-minute drive from Kissimmee, offering sprawling beaches and blue waters where colorful shells arrive with every splash of the surf.

It doesn't matter if you want to indulge your playful side with some water sports on a Spring Break retreat or just relax with the kids beneath the Florida sun, some of the top beaches in the world are delightfully within reach when you stay in Kissimmee.

Explore our guide map below for ideas on where you can enjoy Central Florida's beaches.

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Kissimmee| VisitFlorida.com – Florida Vacation, Tourism …

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Kissimmee is a family-oriented destination known for its off-the-beaten-path adventures, endless sunshine and proximity to world-famous theme parks.

Walt Disney World Resort, Universal Orlando Resort and SeaWorld Orlando are just minutes away. The parks are sure to create experiences that last a lifetime, and with new experiences opening frequently, there is always something different to see.

In addition to the theme parks, there is a wild side to explore in this destination. Lush landscapes create the perfect backdrop for outdoor adventures. Whether its zipping through treetops, gliding across the headwaters of the Everglades or soaring above it all in a hot air balloon, there is sure to be a thrill for you. Natural scenery transforms into three quaint downtown areas that boast boutique shopping, local dining and lakeside parks. If brand name shopping is more your style, Premium Outlets, The Florida Mall and Mall at Millenia offer everything from family favorites to designer shops.

When you're visiting Orlando's theme parks, consider staying and playing in Kissimmee.

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LibertaGia

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Comunicado Libertagia.

Caros membros, como de conhecimento geral a pouco mais de um ano a Libertagia vem em silncio e de portas fechadas, mas agora chegou a hora de lanar luz sobre o ocorrido e de mostrar o processo de recuperao da nossa empresa.

A Libertagia alcanou nmeros muito expressivos, chegamos a mais 2.5 milhes de membros em mais de 160 pases, membros esses que ajudaram a criar uma empresa com propores jamais vistas.

Durante todo esse tempo de silncio a administrao da empresa na Itlia no parou de trabalhar, buscando formas de reerguer a imagem da empresa e toda sua infraestrutura, que foi gravemente abalada por parceiros e fornecedores que no cumpriram com seus respectivos deveres, levando a empresa a um colapso. Todos os envolvidos foram denunciados as autoridades competentes, e agora a Libertagia aguarda reparao dos danos causados (Informaes sobre o andamento do processo sero divulgadas nos prximos comunicados).

Nos prximos 30 dias vamos iniciar um processo de incorporao do novo investidor, corpo jurdico e equipe, seguindo rigorosamente todas as normas internacionais. Durante todo esse processo, as mudanas feitas e etapas a seguir pelos membros sero informadas em nosso site oficial. Tambm divulgaremos os novos produtos, parceiros e o plano compensao em forma de stock option para todos os membros Libertagia.

Atravs de sorteio eletrnico, selecionamos 12 lideres mundiais da Libertagia para fazerem parte de uma reunio que ser realizada no dia 05/06/2016 em Pisa (ITA) onde ser apresentado o novo investidor, produtos, documentos, plataforma e o novo plano da empresa.

Com tudo isso o que buscamos recuperar no s a imagem da empresa mais de todos os membros que acreditam e acreditaram nela, recuperar nossos sonhos e objetivos. hora de unirmos foras em beneficio desta empresa que mais do que nunca pertence de todos ns.

JOIADMIRADA UNIPESSOAL LTD. SOCIETA COOPERATIVA SENZAFRONTIERE. REINALDO M.S.JUNIOR.

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10 Beaches Across Massachusetts – Boston.com

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Who doesnt love a good beach day? Sand between your toes and the relaxing sound of waves rolling in. But do you find yourself wondering which beach you should go to? If you need beach ideas, heres ten across Massachusetts.

Eric Wilbur

At first glance, the town beach in sleepy Menemsha doesnt appear to be much, but when the lights start to go down over Marthas Vineyard, thats when the show begins. Grab fried clams at the nearby popular shack, The Bite, or steamed lobster from the likes of Menemsha Seafood or Larsens Fish Market and settle in for what many consider to be the best spot to watch a sunset in the entire state. The calmer waves at Menemsha Beach also make this a favorite spot for families with young children. For sure, Menemsha is a more serene, genteel alternative to the busier crowds on most other Vineyard beaches.

Eric Wilbur

Well known on the other island for sunsets is Madaket, located at Nantuckets westernmost point. Sunsets are a seasonal treat here, but so too is an early-morning stroll on the quiet beach. The surf can be heavy, as evidenced by the amount of ocean debris on shore. And even though its easily reached by Nantucket public transportation, a $4 round-trip from Nantucket town, the remoteness can sometimes make you feel as though you know a secret kept from other islanders.

Eric Wilbur

The drive down Route 88 can be a headache on any given day, so do yourself a favor and reach this beach by way of Horseneck Road, which runs parallel. Youll discover rolling farm lands, a winery, and a tranquil alternative to the sometimes maddening rush. Once at Horseneck, youll find ample inexpensive parking ($8 for residents, $14 for non-residents), classic Massachusetts dunes, sparkling, clean sand, and new changing facilities. Birders will love the habitat situated here. Camping is also available nearby, featuring 100 sites. Ocean waves can be rough at times, and seaweed can be plentiful, but Horseneck is still a beautiful spot to spend a beach day.

Eric Wilbur

The fine, white sand at Katama Beach, also known as South Beach, plays well in contrast to the deep blue ocean crashing into it with some ferocity at the shore. The three-mile stretch of land stretches far to both left and right, creating a soothing atmosphere where no land is visible as far as the eye can see a welcome escape. The waves can be a bit more aggressive, which makes it an ideal destination for boogie boarding or body surfing.

Eric Wilbur

The sand is a little whiter and brighter at the tip of the Cape, where Provincetowns crown jewel provides the final stop on the historic Cape Cod National Seashore. The views of the Atlantic Ocean are sweeping, and this is a great spot to make an early destination for a spectacular summer sunrise. Waves tend to be light here on an average day, making it a fine spot for families and those not looking to tangle with seaweed.

Eric Wilbur

Parking can be a real issue, as spaces at the beach are reserved for Manchester-by-the-Sea residents only. But you can find limited metered spots in the nearby downtown area if you beat the crowds, and Singing Beach still gets points for accessibility thanks to the presence of the MBTA commuter rail, just a short walk away. The rocky cliff coast of Singing comes into full view upon arrival, giving the area a feel almost like Maine. The pristine sand makes this a favorite North Shore destination, while the stunning views provide a soothing atmosphere.

Eric Wilbur

Located on the south shore of Nantucket, Cisco Beach is a surfing paradise, with waves just gnarly enough to provide the perfect atmosphere for both beginner and experienced boarders alike. This long stretch of sand is backed on one side by eroding dunes, the other by a cool ocean that boasts a number of wetsuits at any time of day. Beginners can learn the craft from the Nantucket Surfing Co., which is on hand for lessons and rentals. No public transportation to Cisco, reachable only by personal vehicle or taxi. Fare from Nantucket town is generally around $14 for one person, one-way. Each additional person will run a few dollars more.

Eric Wilbur

One of the first stops on the Cape Cod National Seashore, this Eastham favorite places annually on beach guru Dr. Stephen Leathermans list of the top 10 cleanest beaches in the country thanks to pristine ocean conditions, fine, powdery sand, and a concerted effort to protect the nesting piping plovers. Adjacent to the beach youll find miles of salt marshes, providing a dramatic backdrop to a Cape Cod jewel. Plentiful parking is available in the nearby parking lot ($15), from which a free National Seashore shuttle bus will whisk you to the shore. Passes for all National Seashore-run beaches are $45 for the season.

Eric Wilbur

Stare out at the ocean from atop this Wellfleet classics sand cliffs for one of Cape Cods most breathtaking views. Then make your way slowly down the adjacent embankment and it will seem like the beach swallowed you into its beauty. The clay-colored cliffs consume you, as if youve been swallowed up by the surroundings. Be sure to stop for lunch at the Beachcomber atop the cliffs, a typical beachside bar. Tip: Park for the day in the Beachcomber lot for $20; youll receive a food or merchandise voucher for the same amount. If thats full, look for additional parking down the road. As with most popular destinations, parking can be tight, so plan to get there early.

Eric Wilbur

This gorgeous stretch of land on the North Shore features fine powdery sand, clear ocean water, and some impressively clean changing and food facilities. The short walk across the parking lot boardwalk to the beach provides beachgoers with a variety of sights, from the plentiful mounds of sand dunes to the immaculate view of one of the states most beloved summer spots. Parking can be pricey $25 on weekends but spots are normally available if you get there early enough. And since the beach stretches for miles, youre not likely to have a difficult time landing a spot in the sand even on the most crowded summer days. If you want to leave the car behind, consider taking the Ipswich Essex Explorer, from the Ipswich commuter rail station. For just $5 round-trip, the shuttle transports passengers and drops them off right in front of the beacheven if the parking lot is full. The ticket price also covers beach admission.

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best and worst beaches in Massachusetts – Julie A. left tips …

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This is my favorite beach on the North Shore. the sand is so soft and fine it's like powder, and it's got a lot of mica in it so it's sparkly and glittery. The beach is kept very clean and for some reason always feels warmer than other North Shore beaches. They have a pretty good consession stand and showers and changing rooms which is a plus if you are traveling a long ways home. The beach has a slow decline, so at low tide you can go out for a mile and still be up to your knees. You can actually bring boogie boards and other 'floatation devices" and just chill in warm water.

As far as I am concerned the beach is separated into 3 areas. The best part, the Private side. If you are not lucky enough to know someone to get over there, you can always drive your boat to it (because we all have one of those) or walk over at low tide. It's around the big rocky point on the left. It is way less crowded, even warmer than the public side and goes on for a nice several mile long walk (with a sweet sandbar detour or two) The public side as two areas. The nice sandy area when you first enter the beach. then to the right there are some great rocks and tide pools, marsh area and then a whole other sandy beach area. No one knows about the beach in the back, so no one is ever there! when the sand gets wet it's turns into quicksand and is fun to walk through. There are 2 weeks in July where the blackheads get a little nasty and annoying, but nothing like at Cranes beach.

This is my favorite beach on the North Shore. the sand is so soft and fine it's like powder, and it's got a lot of mica in it so it's sparkly and glittery. The beach is kept very clean and for some reason always feels warmer than other North Shore beaches. They have a pretty good consession stand and showers and changing rooms which is a plus if you are traveling a long ways home. The beach has a slow decline, so at low tide you can go out for a mile and still be up to your knees. You can actually bring boogie boards and other 'floatation devices" and just chill in warm water.

As far as I am concerned the beach is separated into 3 areas. The best part, the Private side. If you are not lucky enough to know someone to get over there, you can always drive your boat to it (because we all have one of those) or walk over at low tide. It's around the big rocky point on the left. It is way less crowded, even warmer than the public side and goes on for a nice several mile long walk (with a sweet sandbar detour or two) The public side as two areas. The nice sandy area when you first enter the beach. then to the right there are some great rocks and tide pools, marsh area and then a whole other sandy beach area. No one knows about the beach in the back, so no one is ever there! when the sand gets wet it's turns into quicksand and is fun to walk through. There are 2 weeks in July where the blackheads get a little nasty and annoying, but nothing like at Cranes beach.

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Gary Johnson — Libertarian Party Presidential Candidates …

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Good news, disgruntled Americans: As you ponder whether to vote for one of the two most-disliked, dishonest, and morally corrupt politicians ever to run for president Donald Trump and Hillary Clinton you just might have a third option. His name is Gary Johnson. Hes a popular two-term former governor of New Mexico. Hes the likely Libertarian party nominee. And hes set to be on the ballot in the vast majority of states.

Moreover, the short case for him is pretty compelling. Johnson is a self-made man, starting a handyman business in college that grew into a 1,000-employee construction firm. He ran for governor as a fiscal conservative in a blue state, won handily, and can now boast that he cut taxes, vetoed hundreds of bills, presided over significant job growth, balanced the state budget, and created a substantial reserve fund. He won reelection in a rout.

Johnson is an extraordinarily strong gun-rights advocate, and he favors free trade, but hes far too committed to relatively open borders advocating a simpler and more efficient process for entering the country.

He calls himself pro-choice, but hes well to the right of Hillary Clinton supporting late-term abortion bans, parental-notification laws, and opposing public funding for abortion and hes indicated that hed appoint judges who will interpret the Constitution according to its original meaning. He also believes Roe v. Wade was wrongly decided because it expanded the reach of the Federal government into areas of society never envisioned in the Constitution. In the past, Johnson has indicated that hed rather leave abortion policy to the states.

Being generous, one could even call him a sort of peaceful Teddy Roosevelt: an adventurer politician. Hes run dozens of triathlons, hes climbed the highest mountain on all seven continents (including Mount Everest), and he built his house with his own hands. So, on balance, Gary Johnson for president, right?

Not so fast. This being 2016, the world has gone mad, and there are no easy choices left. Johnson is a Libertarian, and that means hes for drug legalization. Thats not necessarily disqualifying. After all, his views are similar to those of the founder of National Review, William F. Buckley Jr., and are shared by a number of conservatives. Johnson, however, is an actual drug user boasting recently that hed just consumed Cheeba Chews, a form of marijuana-infused taffy. (To be fair, he claims that he does not drink alcohol.) But thats not all, not by a long shot.

In 2014, Johnson became a pot entrepreneur. He was named the CEO of Cannibas Sativa. The companys intended products included medicinal oils and get this a cough droplike pot candy. Johnson of course tried and endorsed the product, asking, Why would anybody ever smoke marijuana given this as an alternative?

Oddly, when it comes to religious liberty and the rights of conscience, he may not be libertarian enough. In the Libertarian partys presidential debate, he offered a bizarre and rambling defense of forcing a Jewish baker to bake a Nazi wedding cake in the name of ending religious discrimination. He also briefly endorsed, then walked back, using state power to ban the burqa in the United States. (On the bright side, he did indicate that he understood the political nature of sharia law and its incompatibility with American conceptions of liberty.)

Regarding national defense, hes not as extreme as some libertarians some go so far as to view the rise of jihad as fundamentally Americas fault but he does believe that American military interventions have made the terrorist problem worse. Ive often wondered how a self-defense oriented libertarian would alter American policy once they received a full and complete national-security briefing. Libertarian purists would likely be surprised at the military aggression of a libertarian president. If Johnson were ever elected, wed get to find out.

Make your argument, governor. You can make the case for liberty to a nation embracing authoritarianism. You wont win, but you can matter. This is your moment.

David French is a staff writer at National Review, and an attorney.

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The Futurist: Amazing Future Science & Technology

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Physicists are pretty great at smashing particles such as protons and lead ions together at mind-boggling speeds in particle colliders like the Large Hadron Collider (LHC), but particles are only half the story when it comes to understanding the tiniest interactions that govern the fundamental workings of the Universe. There are also interactions between particles []

Hyperloop Technology A Video featuring the first full-scale demonstration of Hyperloop Technology

Technological Inventions Inc the First drone with virtual reality goggles as well as a mug that regulates the temperature of its contents. Also the only smart key you will ever need and a smart indoor ecosystem that allows you to grow anything with ease. Simplify the way you store and organize your photos by time, []

Here is an amazing new Motorcycle that can turn into aJet Ski in under 5 seconds at the touch of a Button

For the first time, researchers have created a type of stretchy, polymer film that theyre claiming acts like a second skin barrier layer. Thats a big deal, because it means it could one day be used to protect us from sunburn and treat conditions like eczema, but what people are getting really excited about is []

Scientific discovery doesnt get anywhere without collaboration. Even Einstein knew that one genius locked in a room cant solve all the mysteries of the Universe. So when NASA does a patent dump, and allows scientists and engineers from all over the world to get a look at its incredible research, its an awesome thing. This []

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The Futurist: Amazing Future Science & Technology

South Jersey Beaches: From LBI to Cape May Point, NJ

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Updated February 25, 2016.

Each summer hundreds of thousands of visitors from the Philadelphia area, New York, Maryland and Virginia choose to vacation along the world famous Jersey Shore.

Now Atlantic City is one of the world's favorite destinations and the beach differentiates it from any other gaming locale.

During the summer, the beaches are guarded by the country's first lifeguard organization and the water is safe thanks to a stringent State water quality program. More

Over 6.30 square miles in area, the island plays host to year round residents and visitors and home to the Brigantine National Wildlife Refuge - a 20,000-acre national park. Pristine beaches, wonderful waterways and a host of family entertainment venues are found all over the island.

Brigantine Beach is just minutes away from Atlantic City's endless casino entertainment and world famous boardwalk. More

Longport is known as a quiet and peaceful beach community, less hectic than any of the towns to the north, yet still within easy driving distance of the Atlantic City nightlife.

The beach at Longport is small, but impeccably clean with opportunities for sunbathing, kayaking, and surfing. More

Margate beaches are narrower than beaches at many other places at the Jersey Shore. There are a few dunes and those that do exist are artificial. They were created to protect beaches from nor'easters and hurricanes. Most beachfront houses are right against the bulkhead with nothing between them and the ocean. More

Ventnor NJ was also established at the end of the Victorian era, at the turn of the 19th century and has many beautiful Victorian homes and large ocean front homes built 100 years ago.

Ventnor City has large well maintained beaches that are almost always less crowded than their Atlantic City neighbor but still offer visitors and residents the same soft, white sand and great opportunities for swimming, surfing, kayaking and sail boating. More

Avalon beaches were voted by The Washingtonian the safest for swimming out of 30 beaches visited. The magazine called it "the best beach in New Jersey", with its gentle surf, natural dunes, a wide beach, and good life guards. It even has a small and well maintained boardwalk.

Avalon was ranked the seventh best beach in New Jersey in the 2008 Top 10 Beaches Contest sponsored by the New Jersey Marine Sciences Consortium. More

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The Guardian view on artificial intelligence: look out, its …

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A monk comes face to face with his robot counterpart called Xianer at a Buddhist temple on the outskirts of Beijing. Photograph: Kim Kyung-Hoon/Reuters

Google artificial intelligence project DeepMind is building software to trawl through millions of patient records from three NHS hospitals to detect early signs of kidney disease. The project raises deep questions not only about data protection but about the ethics of artificial intelligence. But these are not the obvious questions about the ethics of autonomous, intelligent computers.

Computer programs can now do some things that it once seemed only human beings could do, such as playing an excellent game of Go. But even the smartest computer cannot make ethical choices, because it has no purpose of its own in life. The program that plays Go cannot decide that it also wants a driving licence like its cousin, the program that drives Googles cars.

The ethical questions involved in the deal are partly political: they have to do with trusting a private US corporation with a great deal of data from which it hopes in the long term to make a great deal of money. Further questions are raised by the mere existence, or construction, of a giant data store containing unimaginable amounts of detail about patients and their treatments. This might yield useful medical knowledge. It could certainly yield all kinds of damaging personal knowledge. But questions of medical confidentiality, although serious, are not new in principle or in practice and they may not be the most disturbing aspects of the deal.

What frightens people is the idea that we are constructing machines that will think for themselves, and will be able to keep secrets from us that they will use to their own advantage rather than to ours. The tendency to invest such powers in lifeless and unintelligent things goes back to the very beginnings of AI research and beyond.

In the 1960s, Joseph Weizenbaum, one of the pioneers of computer science, created the chatbot Eliza, which mimicked a non-directional psychoanalyst. It used cues supplied by the users Im worried about my father to ask open-ended questions: How do you feel about your father? The astonishing thing was that students were happy to answer at length, as if they had been asked by a sympathetic, living listener. Weizenbaum was horrified, especially when his secretary, who knew perfectly well what Eliza was, asked him to leave the room while she talked to it.

Elizas latest successor, Xianer, the Worthy Stupid Robot Monk, functions in a Buddhist temple in Beijing, where it dispenses wisdom in response to questions asked through a touchpad on his chest. People seem to ask it serious questions such as What is love?, How do I get ahead in life?; the answers are somewhere between a horoscope and a homily. Since they are not entirely predicable, Xianer is treated as a primitive kind of AI.

Most discussions of AI and most calls for an ethics of AI assume we will have no problem recognising it once it emerges. The examples of Eliza and Xianer show this is questionable. They get treated as intelligent even though we know they are not. But that is only one error we could make when approaching the problem. We might also fail to recognise intelligence when it does exist, or while it is emerging.

The myth of Frankensteins monster is misleading. There might be no lightning bolt moment when we realise that it is alive and uncontrollable. Intelligent brains are built from billions of neurones that are not themselves intelligent. If a post-human intelligence arises, it will also be from a system of parts that do not, as individuals, share in the post-human intelligence of the whole. Parts of it would be human. Parts would be computer systems. No part could understand the whole but all would share its interests without completely comprehending them.

Such hybrid systems would not be radically different from earlier social inventions made by humans and their tools, but their powers would be unprecedented. Constructing and enforcing an ethical framework for them would be as difficult as it has been to lay down principles of international law. But it may become every bit as urgent.

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The Future of Artificial Intelligence – Science Friday

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Skip to content Fusion of human head with artificial intelligence, from Shutterstock

Technologist Elon Musk, Bill Gates, and Steve Wozniak named artificial intelligence as one of humanitys biggest existential risks. Will robots outpace humans in the future? Should we set limits on A.I.? Our panel of experts discusses what questions we should ask as research on artificial intelligence progresses.

Plus,

Stuart Russell

Stuart Russell is a computer science and engineering professor at the University of California, Berkeley in Berkeley, California.

Eric Horvitz

Eric Horvitz is Distinguished Scientist at Microsoft Research and co-director of the Microsoft Research Lab in Redmond, Washington.

Max Tegmark

Max Tegmark is a physics professor at the Massachusetts Institute of Technology in Cambridge, Massachusetts.

Alexa Lim is Science Fridays associate producer. Her favorite stories involve space, sound, and strange animal discoveries.

Should we worry about the imminent rise of robots in our lives?

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This year's SXSW Film festival highlighted our fears about emerging tech and concerns facing online and gaming communities.

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Artificial Intelligence :: Essays Papers

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Artificial Intelligence

The computer revolution has influenced everyday matters from the way letters are written to the methods in which our banks, governments, and credit card agencies keep track of our finances. The development of artificial intelligence is just a small percentage of the computer revolution and how society deals with, learns, and incorporates artificial intelligence. It will only be the beginning of the huge impact and achievements of the computer revolution.

A standard definition of artificial intelligence, or AI, is that computers simply mimic behaviors of humans that would be regarded as intelligent if a human being did them. However, within this definition, several issues and views still conflict because of ways of interpreting the results of AI programs by scientists and critics. The most common and natural approach to AI research is to ask of any program, what can it do? What are the actual results in comparison to human intelligence? For example, what matters about a chess-playing program is how good it is. Can it possibly beat chess grand masters? There is also a more structured approach in assessing artificial intelligence, which began opening the door of the artificial intelligence contribution into the science world. According to this theoretical approach, what matters is not the input-output relations of the computer, but also what the program can tell us about actual human cognition (Ptack, 1994).

From this point of view, artificial intelligence can not only give a commercial or business world the advantage, but also a understanding and enjoyable beneficial extend to everyone who knows how to use a pocket calculator. It can outperform any living mathematician at multiplication and division, so it qualifies as intelligent under the definition of artificial intelligence. This fact does not entertain the psychological aspect of artificial intelligence, because such computers do not attempt to mimic the actual thought processes of people doing arithmetic (Crawford, 1994). On the other hand, AI programs that simulate human vision are theoretical attempts to understand the actual processes of human beings and how they view and interpret the outside world. A great deal of the debate about artificial intelligence confuses the two views, so that sometimes success in artificial intelligence's practical application is supposed to provide structured or theoretical understanding in this branch of science known as cognitive science. Chess-playing programs are a good example. Early chess-playing programs tried to mimic the thought processes of actual chess players, but they were not successful. Ignoring the thoughts of chess masters and just using the much greater computing powers of modern hard wares have achieved more recent successes. This approach, called brute force, comes from the fact that specially designed computers can calculate hundreds of thousands or even millions of moves, which is something no human chess player can do (Matthys, 1995). The best current programs can beat all but the very best chess players, but it would be a mistake to think of them as substantial information to artificial intelligence's cognitive science field (Ptacek, 1994). They tell us almost nothing about human cognitions or thought processes, except that an electrical machine working on different principles can outdo human beings in playing chess, as it can defeat human beings in doing arithmetic.

Assuming that artificial intelligence's practical applications, or AIPA, is completely successful and that society will soon have programs whose performance can equal or beat that of any human in any comprehension task at all. Assume machines existed that could not only play better chess but had equal or better comprehension of natural languages, write equal or better novels and poems, and prove equal or better math and science equations and solutions. What should society make of these results? Even with the cognitive science approach, there are some further distinctions to be made. The most influential claim is if scientists programmed a digital computer with the right programs, and if it had the right inputs and outputs, then it would have thoughts and feelings in exactly the same sense in which humans have thoughts and feelings. In accordance to this view, the computer programming and AICS program is not just mimicking intelligent thought patterns, it actually is going through these thought processes. Again the computer is not just a substitution of the mind. The newly programmed computer would literally have a mind. So if there were an AIPA program that appropriately matched human cognition, scientists would artificially have created an actual mind.

It seems that artificial intelligence is possibly a program that will one day exist. The mind is just the program in hardware of the human brain, but this created mind could also be programmed into computers manufactured by IBM. However, there is a big difference from artificial intelligence and various forms of AICS. Though, it is the weakest claim of artificial intelligence stating that the appropriately programmed computer is a tool that can be used in the study of human cognition. By attempting to impersonate the formal structure of cognitive processes on a computer, we can better come to understand cognition. From this weaker view, the computer plays the same role in the study of human beings that it plays in any other discipline (Taubes, 1995; Crawford, 1994).

We use computers to simulate the behavior of weather patterns, airline flight schedules, and the flow of money in things. No one began programming any of these computer operations so the computer program literally makes brainstorms, or that the computer will literally take off and fly to San Diego when we are doing a computer simulation of airline flights. Also, no one thinks that the computer simulation of the flow of money will give us a better chance at preparing for things like The Great Depression. To stand by the weaker conception of artificial intelligence, society should not think that a computer simulation of cognitive processes actually did any real thinking.

According to this weaker, or more cautious, version of AICS, we can use the computer to do models or simulations of mental processes, as we can use the computer to do simulations of any other process as long as we write a program that will allow us to do so. Since this version of AICS is more cautious, it is probably safe to assume that it is less likely to be controversial, and more likely to be heading towards real possibilities.

Bibliography:

Crawford, Robert, Machine Dreams, Vol. 97, Technology Review, 1 Feb 1994, pp. 77.

Matthys, Erick, Harnessing technology for the future, Vol. 75, Military Review, 1 May 1995, pp. 71

Morss, Ruth, Artificial intelligence guru cultivate natural language, Vol. 14, Boston Business Journal, 20 Jan 1995, pp. 19

Ptacek, Robin, Using artificial intelligence, Vol. 28, Futurist, 1 Jan 1994, pp.38

Taubes, Gary, The rise and fall of thinking machines, Vol. 1995, Inc., 12 Sep 1995, pp. 61

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Artificial Intelligence :: Essays Papers

What Is Artificial Intelligence? (with picture) – wiseGEEK

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burcinc Post 3

I find artificial intelligence kind of scary. I realize that it can be very practical and useful for some things. But I actually feel that artificial intelligence that is developed too far may actually be dangerous to humanity. I don't like the idea of a machine being smarter and more capable than a human.

@SteamLouis-- But artificial intelligence is a part of everyday life. Everything from computer games, to financial analysis software to voice-recognition security systems are types of artificial intelligence. They are forms of weak AI but are artificial intelligence nonetheless.

When people think of AI, robots are the first things to come to mind. And there are huge advancements in this area as well. You may not be familiar with them but there are numerous robots on the market that are very popular. Some act like personal assistants and respond to voice command for various tasks. Others are in the form of house appliances or small gadgets and all serve some sort of use for every day living.

Artificial intelligence doesn't appear to be advancing as quickly as many of us expected. I remember that in the beginning of the 21st century, there was so much speculation about how artificial intelligence, like robots, would become a regular part of our life in this century. Fifteen years down the line, nothing of the sort has happened. Scientists talk about the same thing, but now they're talking about 2050 and beyond. I personally don't think that robots will be a part of regular life even in 2050. Artificial intelligence is not easy to build and use and it's extremely expensive.

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What Is Artificial Intelligence? (with picture) - wiseGEEK

Artificial Intelligence: Foundations of Computational Agents

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We are currently planning a second edition of the book and are soliciting feedback from instructors, students, and other readers. We would appreciate any feedback you would like to provide, including:

Please email David and Alan any feedback you may have.

Artificial Intelligence: Foundations of Computational Agents, Cambridge University Press, 2010, is a book about the science of artificial intelligence (AI). It presents artificial intelligence as the study of the design of intelligent computational agents. The book is structured as a textbook, but it is accessible to a wide audience of professionals and researchers. In the last decades we have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. This book provides the first accessible synthesis of the field aimed at undergraduate and graduate students. It provides a coherent vision of the foundations of the field as it is today. It aims to provide that synthesis as an integrated science, in terms of a multi-dimensional design space that has been partially explored. As with any science worth its salt, artificial intelligence has a coherent, formal theory and a rambunctious experimental wing. The book balances theory and experiment, showing how to link them intimately together. It develops the science of AI together with its engineering applications.

You can search the book and the website:

We are requesting feedback on errors for this edition and suggestions for subsequent editions. Please email any comments to the authors. We appreciate feedback on references that we are missing (particularly good recent surveys), attributions that we should have made, what could be explained better, where we need more or better examples, topics that we should cover in more or less detail (although we are reluctant to add more topics; we'd rather explain fewer topics in more detail), topics that could be omitted, as well as typos. This is meant to be a textbook, not a summary of (recent) research.

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Artificial Intelligence: Foundations of Computational Agents

Artificial intelligence research and development – AI. Links999.

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Many academic institutions, companies and corporations worldwide are involved in artificial intelligence research. While some focus exclusively on the hardware aspect of robotic machinery and androids - such as the prosthetics involved in creating elbow and knee joints and the artificial intelligence needed to control these, for example, others are focused on the workings of the artificial mind, creating deductive reasoning and other complex issues that mimic our own brain and our physical neural network.

Hardware issues of artificial intelligence can be the control of a body, as in the case of an intelligent, humanoid, robot. But also the hard-wiring of a simulated brain, as is the case with Asimov's "positronic" brain, or the brain of "Data", the android in the Star Trek television series.

Software issues can involve logic, action-reaction, response, speech and visual recognition tasks and of course the programming languages needed to write these programs.

Designing and creating a neural network similar to our own is one of the most difficult aspects of creating an artificial intelligence (see also Neural Networks, Nanotechnology and Robotics). This approach requires both hardware and software or wetware, also known as biological hardware.

The human neural network is a vastly complex development spanning millions of years of biological evolution with the core going back maybe a billion years or more, to the very first "life" form.

Most parts of this network are autonomous and require no conscious thought. If we had to consciously tell our bodies to breathe air, pump blood or instruct muscles to contract or relax for movement and other bodily functions, we wouldn't be here. It would be impossible.

Thus much of our functioning is subconscious and autonomous, with only our reasoning mind, and our "self", whatever that may be, in need of constant attention.

Designing an artificial intelligence of this complexity is not possible with our current technological knowledge and we may never achieve anything closely resembling it. (Unless, of course, we design intelligent machines to do it for us.)

Do we need artificial intelligence?

With a growing world population, many of which are unemployed and uneducated, do we really need artificial intelligences that cost billions to research and to build? Wouldn't it be better to spend all that money on developing the human condition instead?

The simple answer would be Yes. In order to create a more level playing field for humanity we really need to educate those that lack education and provide positive employment for them. With all that brain power available who needs artificial intelligences. But that is easier said than done.

Until we have a unified world government that would allocate resources on a more equal scale it doesn't seem likely. Countries with the highest unemployment and lowest educational level generally suffer from inept and corrupt governments, and under current international agreements, there is no interference in internal affairs.

The best we can do is let advanced nations develop advanced technologies, such as artificial intelligence, and use these developments at some future time to aid our poorer fellow humans.

So perhaps we don't need artificial intelligence but it may provide the way to a better future for all of us.

See also: Neural Networks, Nanotechnology and Robotics.

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The Personality Forge AI – Artificial Intelligence Chat Bots

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350

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April 8, 2016

Data and Code Upgrade

I just completed a data and code upgrade. It touched on every part of the site. I did thorough testing and fixed everything I found (and fixed and upgraded many areas, too) but if you run into anything weird, from broken links to troubles with AIScript, memories, etc, for the time being please email me directly at benji@personalityforge.com rather than using the Bug Reporting tool. Thanks!

March 23, 2016

Backup Your Bots From Time to Time

Remember to export your chat bot from time to time when you're working on it. This allows you to restore it should anything happen - be it a the rare server crash with data loss, or accidentally deleting Keyphrases or Seeks during development.

Welcome to the The Personality Forge, an advanced artificial intelligence platform for creating chat bots. The Personality Forge's AI Engine integrates memories, emotions, knowledge of hundreds of thousands of words, sentence structure, unmatched pattern-matching capabilities, and a scripting language called AIScript. It's easy enough for someone without any programming experience to use. Come on in, and chat with bots and botmasters, then create your own artificial intelligence personalities, and turn them loose to chat with both real people and other chat bots. Here you'll find thousands of AI personalities, including bartenders, college students, flirts, rebels, adventurers, mythical creatures, gods, aliens, cartoon characters, and even recreations of real people.

Personality Forge chat bots form emotional relationships with and have memories about both people and other bots. True language comprehension is in constant development, as is a customizable Flash interface. Transcripts of every bot's conversations are kept so you can read what your bot has said, and see their emotional relationships with other people and other bots.

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The Personality Forge AI - Artificial Intelligence Chat Bots

AI Overview | AITopics

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Broad Discussions of Artificial Intelligence

Exactly what the computer provides is the ability not to be rigid and unthinking but, rather, to behave conditionally. That is what it means to apply knowledge to action: It means to let the action taken reflect knowledge of the situation, to be sometimes this way, sometimes that, as appropriate...

In sum, technology can be controlled especially if it is saturated with intelligence to watch over how it goes, to keep accounts, to prevent errors, and to provide wisdom to each decision. --- Allen Newell, from Fairy Tales

If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."

The National Academy of Science offers the following short summary of the field: "One of the great aspirations of computer science has been to understand and emulate capabilities that we recognize as expressive of intelligence in humans. Research has addressed tasks ranging from our sensory interactions with the world (vision, speech, locomotion) to the cognitive (analysis, game playing, problem solving). This quest to understand human intelligence in all its forms also stimulates research whose results propagate back into the rest of computer sciencefor example, lists, search, and machine learning." From Section 6: Achieving Intelligence of the 2004 report by the Computer Science and Telecommunications Board (CSTB) Computer Science: Reflections on the Field, Reflections from the Field (2004).

However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) . . .

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Artificial Intelligence: Learning to Learn – Education

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2011 VIRTUAL SCIENCE FAIR ENTRY

The purpose of this project was to determine the best algorithm for strategy games.

Computer Science

9thGrade

Requires technical knowledge

There are no costs associated with this project.

There are no safety hazards associated with this project.

The total time taken to complete this project is as follows:

The purpose of this project was to determine the best algorithm for strategy games.

The goal of endowing human-like intelligence to inanimate objects has a long history. Modern computers can perform millions of calculations per second, but even with all of this remarkable speed, true logic has yet to be achieved. Every year that passes, computers come closer and closer to achieving this goal, or at least mimicking true logic. Game strategy is one of the most common applications of artificial intelligence. Algorithms are a set of instructions a computer follows to achieve a task or goal. There are three main types of algorithms for intelligence in games: Alpha-beta, learning, and hybrids. Chess was one of the first games to implement artificial intelligence with the discovery of the Alpha-beta algorithm in 1958 by scientists at Carnegie-Mellon University (Friedel, n.d.). The Alpha-beta algorithm was the first feasible algorithm that could be used for strategy in games. As artificial intelligence in games evolved and became more complex, a more modern learning approach has been adopted. Even though there have been major advancements in both learning style algorithms and Alpha-beta algorithms; a hybrid utilizing elements of both algorithms results in a stronger, more efficient, and faster program. On the forefront of the quest for artificial intelligence, these algorithms are playing vastly important roles.

The Alpha-beta algorithm has a long history of success. The first use of the algorithm in a game was in the 70s and 80s by the Belle computer. Belle remained the champion of computer chess until being superseded by the Cray supercomputer (Friedel, n.d.). Belle was the first computer to be successful using the early forms of the Alpha-beta algorithm. Deep Blue later used the algorithm in order to defeat chess grandmaster Garry Kasparov; this was a major development for the artificial intelligence community as it was the first time in history a computer had beaten a chess grandmaster in a standard match. Over time, the algorithm has been revised, updated, and modified to the point where several versions of the algorithm exist that all use the same core principles.

The Alpha-beta algorithm uses brute-force calculations (thousands every second) to make decisions. The Alpha-beta algorithm uses the minimax principle (one player tries to maximize their score while the other tries to minimize it) and efficient evaluation techniques in order to achieve its logic. Alpha-beta is a game tree searcher, or in other words, it forms a hierarchy of possible moves down to a defined level (i.e. six moves). In some variations, eliminating symmetries and rotations is used to reduce the size of the game tree (Lin, 2003). After the tree is formed the algorithm then proceeds to evaluate each position in the tree based on a set of rules intended to make the computer play stronger, this is called heuristics. The reason why Alpha-beta is fast, yet strong is that it ignores portions of the game board (Lin, 2003). It decides which portions to ignore based on finding the best move per level (or move) and ignoring all the moves that arent the best and the moves under them. Alpha-beta can calculate two levels of moves with 900 positions in 0.018 seconds, three levels of moves with 27,000 positions in 0.54 seconds, four levels of moves with 810,000 positions in 16.2 seconds, and so on. These efficiency-improving techniques are responsible for the small calculation times and improved game strategy that the algorithm provides.

Learning style algorithms are another popular type of algorithm for game use. Learning style algorithms arent necessarily a recent creation. They have been in use for approximately thirty years, but have been met with limited success until recently. In this approach, an algorithm uses its own experiences, or a large database of pre-played games to determine the best moves. Unfortunately, learning algorithms have also incorporated the bad strategies utilized by novice players. Over time, improvements have been made so that an algorithm can be a threat to intermediate players in most action games; however, learning algorithms are often unsuccessful in games requiring strategic play. The Chinook program uses the most notable learning algorithm. The program spent eighteen years calculating every possible move for the game of checkers. But the Chinooks algorithm is considered by some not to be a true learning algorithm since it already knows all of the possible outcomes for every move (Chang, 2007). Chinook, however, does adjust its playing style for each players strategy; this is where its element of learning comes into play (Chang, 2007). Learning algorithms are considered closer to true intelligence than other algorithms that use brute-force calculations such as Alpha-beta. Compared to pure calculation algorithms, they play games more like humans and even show very limited aspects of creativity and self-formed strategy.

A hybrid algorithm combines the brute-force style of the Alpha-beta algorithm with the flexibility of the learning style algorithm. This method insures that the full ability of the computer is used while it is free to adapt to each players individual game style. Chinook successfully utilized this technique to make a program that is literally unbeatable. Because of the Chinook program, the game of checkers has been solved. No matter how well an opponent plays, the best they can do is end in a draw (Chang 2007).

Other champion programs have used just one style of algorithm in order to win. As a result, no particular algorithm has been measured or proven to be dominant. Game developers choose which algorithm to use based largely on personal preferences and on a lack of consensus from the artificial intelligence community as to which algorithm is superior. There are weaknesses that can be used to determine which algorithm will prove to be inferior. For example, the Alpha-beta algorithm does not generate all possible moves from the current condition of the game. Alpha-beta assumes that the opponent will make the best possible move available. If a player makes a move that is not in their best interest, the algorithm will not know how to respond because that moves game tree has not been calculated. The opponent can trick the algorithm by making sup-par moves, and forcing it to recalculate. It is also important to note that the Alpha-beta algorithm can use tremendous amounts of time when calculating more than a couple of moves. The learning algorithm has its flaws, too. If it encounters an unknown strategy, the algorithm will be helpless against its opponents moves. The most likely way to minimize these flaws is to combine these algorithms into a hybrid. If the hybrid encounters an unknown strategy, it can then use the Alpha-beta style game tree to determine the possible moves from that point. Likewise, if the opponent uses a move not calculated by the brute-force method, it can then use learned strategies to defend itself. The hybrid algorithm will be faster and have better winning strategies than either the Alpha-beta, or the learning style algorithms.

The experiment clearly demonstrated the alpha-beta algorithm won more games, took less time to generate a move, and took less moves to win. It was clearly superior to both the hybrid and learning algorithms.

This chart shows the percent each algorithm won out of 9,000 games of checkers. Alpha-beta scored the highest percentage of wins, the hybrid came in second, and the learning algorithm scored the lowest percentage.

This chart displays the average time it took each algorithm to generate a move. In this situation the lowest scoring algorithm preformed the best.

This chart represents the average number of moves it took each algorithm to win a game. As with the previous chart, the lowest scoring algorithm performed the best.

Evidence gathered from the experiments showed that the Alpha-beta algorithm was far superior to both the hybrid and learning algorithms. This can be concluded based on three distinct factors: the percentage of wins, the average time taken to make a move, and the average number of moves generated in order to win a game. In each of these categories the Alpha-beta algorithm preformed the best in every category. The hybrid performed better than the learning, but worse than the Alpha-beta. The Learning algorithm performed the worst.

This experiment included 9,000 trials; therefore, the experimental error was minimal. The only measured value that needed to be considered for errors was the average amount of time each algorithm used to generate a move. The computer can record the precise time, but the time was rounded so the time-keeping process would not affect the outcome of an experiment. However, the difference between the averages was not at all significant, and even if the computer recorded the results with absolute precision the conclusion would remain unchanged. Another aspect to consider about the results was the possibility of a recursion loop (basically, when the algorithm gets stuck in a repeating loop). Although the algorithm will break from the loop, it would cause the average time spent on a move to go up considerably for that game. The last error that needed to be considered was the inefficiencies in an algorithms programming. If an algorithm was erroneously programmed in a way that was inefficient, it would obviously damage the overall performance.

Chang, K.(2007, July 19). Computer checkers program is invincible.Retrieved from http://www.nytimes.com/2007/07/19/science/19cnd-checkers.html

Frayn, C.(2005, August 1). Computer chess programming theory. Retrieved from http://www.frayn.net/beowulf/theory.html

Friedel, F.(n.d.).A short history of computer chess. Retrieved from http://www.chessbase.com/columns/column.asp?pid=102

Lin, Y. (2003).Game trees. Retrieved from http://www.ocf.berkeley.edu/~yosenl/extras/alphabeta/alphabeta.html

For a demo of the program email connerruhl at me.com

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Artificial Intelligence: Learning to Learn - Education

Academia.edu | Documents in Artificial Intelligence …

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A Field Programmable Gate Array (FPGAs) is a small Field Programmable Device (FPD) that supports thousands of logic gates. FPGA is a high speed, low cost, short time to market and small device size. Technically speaking an FPGA can be... more

A Field Programmable Gate Array (FPGAs) is a small Field Programmable Device (FPD) that supports thousands of logic gates. FPGA is a high speed, low cost, short time to market and small device size. Technically speaking an FPGA can be used to solve any problem which is computable. This is trivially proven by the fact FPGA can be used to implement a Soft microprocessor. Their advantage lies in that they are sometimes significantly faster for some applications due to their parallel nature and optimality in terms of the number of gates used for a certain process. Specific applications of FPGAs include digital signal processing, software-defined radio, ASIC prototyping, medical imaging, computer vision, speech recognition, nonlinear control, cryptography, bioinformatics, computer hardware emulation, radio astronomy, metal detection and a growing range of other areas. Traditionally, FPGAs have been reserved for specific vertical applications where the volume of production is small. For t...

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Principles of Artificial Intelligence: Study Guide

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Course Information Course Materials AI Resources Quick Links

Principles of Artificial Intelligence: Study Guide

Modeling dependence between attributes. The decision tree classifier. Introduction to information theory. Information, entropy, mutual information, and related concepts (Kullback-Liebler divergence).

Algorithm for learning decision tree classifiers from data. The relationship between MAP hypothesis learning, minimum description length principle (Occam's razor) and the role of priors.

Ovrfitting and methods to avoid overfitting -- dealing with small sample sizes; prepruning and post-pruning. Pitfalls of entropy as a splitting criterion for multi-valued splits. Alternative splitting strategies -- two-way versus multi-way splits; Alternative split criteria: Gini impurity, Entropy, etc. Cost-sensitive decision tree induction -- incorporating attribute measurement costs and misclassification costs into decision tree induction.

Dealing with categorical, numeric, and ordinal attributes. Dealing with missing attribute values during tree induction and instance classification.

Evaluation of classifiers. Accuracy, Precision, Recall, Correlation Coefficient, ROC curves.

Required Readings

Recommended Readings

Introduction to Artificial Neural Networks and Linear Discriminant Functions. Threshold logic unit (perceptron) and the associated hypothesis space. Connection with Logic and Geometry. Weight space and pattern space representations of perceptrons. Linear separability and related concepts. Perceptron Learning algorithm and its variants. Convergence properties of perceptron algorithm. Winner-Take-All Networks.

Bayesian Recipe for function approximation and Least Mean Squared (LMS) Error Criterion. Introduction to neural networks as trainable function approximators. Function approximation from examples. Minimization of Error Functions. Derivation of a Learning Rule for Minimizing Mean Squared Error Function for a Simple Linear Neuron. Momentum modification for speeding up learning. Introduction to neural networks for nonlinear function approximation. Nonlinear function approximation using multi-layer neural networks. Universal function approximation theorem. Derivation of the generalized delta rule (GDR) (the backpropagation learning algorithm).

Generalized delta rule (backpropagation algorithm) in practice - avoiding overfitting, choosing neuron activation functions, choosing learning rate, choosing initial weights, speeding up learning, improving generalization, circumventing local minima, using domain-specific constraints (e.g., translation invariance in visual pattern recognition), exploiting hints, using neural networks for function approximation and pattern classification. Relationship between neural networks and Bayesian pattern classification. Variations -- Radial basis function networks. Learning non linear functions by searching the space of network topologies as well as weights.

Lazy Learning Algorithms. Instance based Learning, K-nearest neighbor classifiers, distance functions, locally weighted regression. Relative advantages and disadvantages of lazy learning and eager learning.

Additional Information

The material to be covered each week and the assigned readings (along with online lecture notes, if available) are included on this page. The study guide (including slides, notes, readings) will be updated each week. The assigned readings are divided into required and recommended readings and notes from recitations (if available). You will be responsible for the material covered in the lectures and the assigned required readings. You are strongly encouraged to explore the recommended readings.

Overview of the course; Overview of artificial intelligence: What is intelligence? What is artificial intelligence (AI)? History of AI; Working hypothesis of AI. Introduction to intelligent agents. Intelligent agents defined. Taxonomy of agents. Simple reflex agents (memoryless agents); agents with limited memory; rational agents; agents with goals; utility-driven Agents.

You may skip most of these readings if you have prior programming experience in Java.

Goal-Based Agents. Problem-solving as state space search. Formulation of state-space search problems. Representing states and actions. Basic search algorithms and their properties: completeness, optimality, space and time complexity. Breadth-first search, depth-first search, backtracking search, depth-limited and interative deepening search.

Heuristic search. Finding optimal solutions. Best first search. A* Search: Adding Heuristics to Branch and Bound Search. Completeness, Admissibility, and Optimality of the A* algorithm. Design of admissible heuristic functions. Comparison of heuristic functions ("informedness" of heuristics).

Problem Solving through Problem Reduction. Searching AND-OR graphs. A*-like admissible algorithm for searching AND-OR graphs.

Problem solving as Constraint Satisfaction. Properties of constraint satisfaction problems. Examples of constraint satisfaction problems. Iterative instantiation method for solving CSPs. Scene interpretation as constraint propagation (Waltz's line labeling algorithm). Node consistency, arc consistency, and related algorithms.

Stochastic search: Metropolis Algorithm, Simulated Annealing, Genetic Algorithms.

Introduction to Knowledge Representation. Logical Agents with explicit knowledge representation. Knowledge representation using propositional logic; Review of Propositional Logic: Propositional logic as a knowledge representation language: Syntax and Semantics; Possible worlds interpretation; Models and Logical notions of Truth and Falsehood; Logical Entailment; Inference rules; Modus ponens; Soundness and Completeness properties of inference. Modus Ponens is a sound inference rule for Propositional logic, but is not complete. Extending modus ponens - the resolution principle.

Logical Agents without explicit representation. Comparison of logical agents with and without explicit representations.

FOPL (First-Order Predicate Logic). Ontological and epistemological commitments and Syntax and semantics of FOPL. Examples. Theorem-proving in FOL. Unification, instantiation, and entailment.

Transformation of FOPL sentences in Clause Normal Form. Resolution by refutation for First Order Predicate Logic. Examples. Automated Theorem Proving. Search Control Strategies for Theorem Proving. Unit Preference, Set of Support and related approaches. Soundness and Completeness of Proof Procedures. Semidecidability of FOPL and its implications. Brief discussion of Datalog (for deductive databases) and Prolog (for logic programming).

Emerging Applications of Knowledge Representation.. Semantics-Driven Applications. Ontologies. Information Integration. Service Oriented Computing. Semantic Web. Brief overview of Ontology Languages: RDF, OWL. Description Logics - Syntax, Semantics, and Inference.

Representing and Reasoning Under Uncertainty. Review of elements of probability. Probability spaces. Bayesian (subjective) view of probability. Probabilities as measures of belief conditioned on the agent's knowledge. Axioms of probability. Conditional probability. Bayes theorem. Random Variables. Independence. Probability Theory as a generalization of propositional logic. Syntax and Semantics of a Knowledge Representation based on probability theory. Sound inference procedure for probabilistic reasoning.

Independence and Conditional Independence. Exploiting independence relations for compact representation of probability distributions. Introduction to Bayesian Networks. Semantics of Bayesian Networks. D-separation. D-separation examples. Answering Independence Queries Using D-Separation tests.

Probabilistic Inference Using Bayesian Networks. Exact Inference Algorithms - Variable Elimination Algorithm; Message Passing Algorithm; Junction Tree Algorithm. Complexity of Exact Bayesian Network Inference. Approximate inference using stochastic simulation (sampling, rejection sampling, and liklihood weighted sampling

Making Simple Decisions under uncertainty, Elements of utility theory, Constraints on rational preferences, Utility functions, Utility elicitation, Multi-attribute utility functions, utility independence, decision networks, value of information

Mid term examination

Sequential Decision Problems. Markov Decision Processes. Value Iteration. Policy Iteration. Partially Observable MDPs.

Markov Decision Processes and Sequential Decision Problem.

Reinforcement Learning. Agents that learn by exploring and interacting with environments. Examples of reinforcement learning scenario. Markov decision processes. Types of environments (e.g., deterministic versus stochastic state transition functions and reward functions, stationary versus non-stationary environments, etc.).

The credit assignment problem. The exploration vs. exploitation dilemma. Value Iteration algorithm. Policy Iteration algorithm. Q-learning Algorithm, Confergence of Q-learning. Temporal Difference Learning Algorithms.

Recommended readings

Additional Information

Overview of machine learning. Why should machines learn? Operational definition of learning.

Bayesian Decision Theory. Optimal Bayes Classifier. Minimum Risk Bayes Classifier.

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Principles of Artificial Intelligence: Study Guide

Artificial Intelligence in Games – CodeProject

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This article is written by Janusz Grzyb, and was originally published in the June 2005 issue of the Software Developer's Journal magazine. You can find more articles at the SDJ website.

Elements of artificial intelligence used in computer games have come a long way. In the beginning, the developed systems were based on sets of rules written directly in the code of the game or on the behaviour scripts interpreted by the code, with the whole thing based most commonly on the appropriate selection of importance of the random factor in the process of choosing the appropriate behaviour. That time witnessed the birth of such memorable games as the immortal River-Raid, Donkey-Kong, Boulder-Dash, and many other objects of fascination for users of eight-bit machines, back in the 1970s.

Another step in the development process was introducing simple computer science methods, such as the still popular and frequently used Finite State Machine method, into describing the behaviour of the computer-controlled enemies. However, as the demands of the players grew day by day, games grew more and more complicated, thanks to the use of more and more advanced computing algorithms. The dawn of the era of RTS-type games (Real Time Strategy) has caused a significant shift of interest (in terms of frequency of use) to algorithms which determine the optimal path between two specified points on a map.

Fast technical progress and rapid increase of processing power of home computers were also a catalyst for the development of applications using artificial intelligence in computer games. The first games and artificial intelligence algorithms had to settle for limited capabilities of machines available at that time, with the processor frequencies no higher than 2 MHz. The first PCs brought in new possibilities and new applications. After PCs with 386/486 processors became the standard for a home computer, programmers were given new possibilities; that led to the start of a race between game development companies. For a long time, the foremost indicator of a computer game's quality was the quality of three-dimensional graphics it featured; however, a realisation soon came that nice graphics, sound, and character animation is not everything. Recently, one of the most important elements of computer games has been identified as artificial intelligence as the primary factor behind the so-called playability of present-day video games.

The process of production of computer games has undergone significant changes as well. Even though programming the artificial intelligence of a game used to be treated slightly unfairly, and its implementation tended to be pushed to near the end of the production of the game's engine, at present, planning the modules of artificial intelligence and their co-operation with other components of the game is one of the most important elements of the planning process.

More and more frequently, at least one of the members of a programming team is designated to, full-time and ever since the beginning of the project, handle designing and programming the modules of artificial intelligence.

At present, when in most homes, one can find PC-class computers with Pentium IV processors with frequencies in the range of 3 to 4 GHz, it is being considered to let computer games make use of the most advanced and sophisticated methods of artificial intelligence: neural networks, genetic algorithms, and fuzzy logic. In the age of Internet and network games, artificial intelligence systems in games have been given new tasks: a computer player should, in its behaviour and strategies of playing, be indistinguishable from a real player on the other side of an Internet connection.

While discussing the evolution of artificial intelligence in computer games, one definitely should mention the games which have turned out to be milestones in the development of intelligent behaviour in games.

One of the most popular games of the 1990s was WarCraft a game developed by the Blizzard studio. It was the first game to employ path-finding algorithms at such a grand scale, for hundreds of units in the game engaged in massive battles. SimCity, created by the company Maxis, was the first game to prove the feasibility of using A-Life technologies in the field of computer games. Another milestone turned out to be the game Black&White, created in 2001 by Lionhead Studios, in which technologies of computer-controlled characters' learning were used for the first time.

FPS-type games usually implement the layered structure of the artificial intelligence system. Layers located at the very bottom handle the most elementary tasks, such as determining the optimal path to the target (determined by a layer higher up in the hierarchy) or playing appropriate sequences of character animation. The higher levels are responsible for tactical reasoning and selecting the behaviour which an AI agent should assume in accordance with its present strategy.

Path-finding systems are usually based on graphs describing the world. Each vertex of a graph represents a logical location (such as a room in the building, or a fragment of the battlefield). When ordered to travel to a given point, the AI agent acquires, using the graphs, subsequent navigation points it should consecutively head towards in order to reach the specified target location. Moving between navigation points, the AI system can also use local paths which make it possible to determine an exact path between two navigation points, as well as to avoid dynamically appearing obstacles.

The animation system plays an appropriate sequence of animation at the chosen speed. It should also be able to play different animation sequences for different body parts: for example, a soldier can run and aim at the enemy, and shoot and reload the weapon while still running. Games of this kind often employ the inverted kinematics system. An IK animation system can appropriately calculate the parameters of arm positioning animation so that the hand can grab an object located on, e.g., a table or a shelf. The task of modules from higher layers is to choose the behaviour appropriate for the situation for instance, whether the agent should patrol the area, enter combat, or run through the map in search of an opponent.

Once the AI system has decided which behaviour is the most appropriate for the given situation, a lower-level module has to select the best tactics for fulfilling that task. Having received information that the agent should, for instance, fight, it tries to determine the approach that is the best at the moment e.g., whether we should sneak up on the opponent, hide in a corner and wait for the opponent to present a target of itself, or perhaps just run at him, shooting blindly.

In RTS-type games, it is possible to distinguish several modules of the artificial intelligence system and its layered structure. One of the basic modules is an effective path-finding system sometimes, it has to find a movement solution for hundreds of units on the map, in split seconds and there is more to it than merely finding a path from point A to point B, as it is also important to detect collisions and handle the units in the battlefield avoid each other. Such algorithms are typically based on the game map being represented by a rectangular grid, with its mesh representing fixed-sized elements of the area. On higher levels of the AI system's hierarchy, there are modules responsible for economy, development or, very importantly, a module to analyse the game map. It is that module which analyses the properties of the terrain, and a settlement is built based on the assessment, e.g., whether the settlement is located on an island, thus requiring higher pressure on building a navy. The terrain analyser decides when cities should be built and how fortifications should be placed.

Figure 1. Representation of the world in a RTS-type game

Figure 2. Representation of the world in a FPS-type game

Basically, in the case of most sports games, we are dealing with large-scale cheating. Take car racing games, for instance. For the needs of the AI, from the geometry of the game map, only and only the polygons belonging to the track of a computer-controlled opponent should travel on and get distinguished. Two curves are then marked on that track: the first represents the optimal driving track, the second the track used when overtaking opponents. The whole track gets split into appropriately small sectors and, having taken parameters of the surface into account, each element of the split track gets its length calculated. Those fragments are then used to build a graph describing the track, and to obtain characteristics of the road in the vehicle's closest vicinity. In effect, the computer knows it should slow down because it's approaching the curve, or knows that it's approaching an intersection and can, e.g., take a shortcut. Two important attributes of Artificial Intelligence systems in such games is being able to analyse the terrain in order to detect obstacles lying on the road, and strict co-operation with the physics module. The physics module can provide information that the car is skidding, having received which the Artificial Intelligence system should react appropriately and try to get the vehicle's traction back under control.

Figure 3. The method of presentation of reality in car race (segmentation and optimalisation of the track)

Figure 4. The method of presentation of reality in car race

Similar cheating can also be found in other sports games. In most cases, a computer-controlled player has its complete behaviour determined even before the beginning of the turn that is, it will, e.g., fall over while landing (acrobatics, ski jumping etc.), have the wrong velocity, start false etc. Additionally, in games simulating sports with scoring by judges, the scores are generated according to the rules defined by the appropriate sports bodies.

The predefined scenario of a computer-controlled player is then acted out by the character animation system.

In the following part of the article, I would like to discuss the two most popular algorithms used in programming computer games. Possessing knowledge about them, one can successfully design a simple artificial intelligence system fulfilling the needs of simple FPS or RTS games. The first of the two is the A-Star algorithm, used in performing fast searches for the optimal path connecting two points on the map (graph) of a game. The other is the finite state machine, useful, e.g., in preparing behaviour scenarios for computer-controlled opponents, typically delegating its low-level tasks to a path-finding module.

The problem of finding a way from point A to point B on a map is a key problem in almost any computer game (possibly not counting certain sports games and some other types of games which can be counted using the digits of one hand). At the same time, algorithms from this group belong to the lower level of the games' AI, serving as a base for constructing more complicated and more intelligent types of behaviour, such as strategic planning, moving in formations or groups, and many others. This issue has already been thoroughly evaluated in the world of computer games, with one algorithm A* having become a present-day standard.

The world of almost any computer game can be represented with a graph, its form depending on the kind of the game. In RTS-type games, the world is typically represented by a two-dimensional array, each of its elements corresponding to fragments of the game world's rectangular map. Each element (except boundary ones) has eight neighbours. Using such a representation of the RTS world, we can construct a graph in which every element of the 2D array will be corresponded to by one vertex of the graph. The edges of the graph (typically present only between the nearest neighbours) illustrate the possibility (or lack thereof) of moving from one of the elements of the map to the neighbouring element. In real-time strategies, we usually assign one vertex of the graph to an area the smallest unit in the game can fit into.

In FPS-type games, the vertices of the graph are typically locations/rooms, with the graph's vertices denoting the existence of a direct connection between the two rooms.

There are a lot of algorithms for finding the optimal path in a graph. The most simple of such algorithms, commonly called fire on the prairie, works by constructing consecutive circles around the starting point, with each step of the algorithm building another, wider circle. Consecutive circles and elements belonging to them are assigned larger and larger indices. As one can see in Figure 5, the circle with index 4 passes through our target point.

Figure 5. A simple path-finding algorithm

Now, heading in the opposite direction and following the rule that in each step we move to the nearest map point located on the circle with a smaller index, we reach the starting point; the elements of our map we have returned through make up the shortest path between the starting point and the destination.

Examining the way this algorithm works, one can see that, in addition to its great advantage the simplicity it also possesses a severe drawback. The path the algorithm has found in our example consists of only five elements of the game world, even though 81 fields of the map would have to be examined in the worst-case scenario. In case of a map consisting of 256x256 fields, it might mean having to examine 65536 map elements!

Enter A* and its primary advantage minimisation of areas being examined by consciously orienting the search towards the target. Keeping it brief, I could say that, when calculating the cost of reaching a point on the map, the A* algorithm adds to it some heuristics indicating the estimated cost of reaching the destination; this function is typically the distance to the destination from the point currently being examined.

Many requirements are presented to optimal path-finding systems. Optimal does not necessarily mean the shortest; the algorithm can take into account such additional factors as the type of the terrain (for instance, a tank in a RTS game will pass the swamp faster going around it than traversing it), turning angle limitations, the number of enemies in the area, and many other elements depending on the particular game. The algorithm should avoid uncrossable areas of the map or, for example, maintain distance from friendly units. The foremost requirement is that the algorithm should always be able to find the optimal path, as long as a path between the two points exists. Listing 1 presents the pseudocode describing the A* algorithm.

The algorithm applied directly may turn out to be ineffective as a result of how much time operations on the structures from the priority queue (the OpenList) and the ClosedList can take. Multiple programming methods exist which work around those imperfections. Optimisation issues can be approached from two ways:

In the first case, one often applies the method of dividing the whole world (map) into regions and splitting the algorithm into two sections: first, we search for the path by checking which regions we should go through; then for each region, we move from the entry point to the exit. Within each region, we find the optimal path, using the A* locally for the region we are in. That way, we significantly limit the search area, thus decreasing the amount of resources required for calculations.

In fact, this method is strongly based on how a human looks for a way to the target when traveling to another end of a large city, a walker doesn't plan the whole route with equal precision; instead, he/she travels between known orientation points, planning precisely the way between each two points, up to the street he/she is going to walk.

Another optimisation factor is the appropriate choice of functions and parameters for heuristics, as this is what decides how much the search area spreads over the game map.

Finite state machines are one of the least complicated, while at the same time, one of the most effective and most frequently used methods of programming artificial intelligence. For each object in a computer game, it is possible to discern a number of states it is in during its life. For example: a knight can be arming himself, patrolling, attacking, or resting after a battle; a peasant can be gathering wood, building a house, or defending himself against attacks. Depending on their states, in-game objects respond in different ways to (the finite set of) external stimuli or, should there be none, perform different activities. The finite state machine method lets us easily divide the implementation of each game object's behaviour into smaller fragments, which are easier to debug and extend. Each state possesses code responsible for the initialisation and deinitialisation of the object in that state (also often referred to as the state transition code), code executed in the game's each frame (e.g., to fulfill the needs of artificial intelligence functions, or to set an appropriate frame of animation), and code for processing and interpreting messages coming from the environment.

Finite state machines are typically implemented using one of the two following methods:

In the age of object-oriented design and programming, the first method is being phased out by the second, i.e., machines implemented on the basis of a project pattern state. Here is an example of such an object-oriented machine, describing the partially possible behaviour of a knight; each state of the object is represented by an abstract base class:

All classes deriving from this class define the behaviour in each state (Listings 2, 3, and 4). An in-game object possesses a pointer to the object of its present state's base class and a method assisting the state transition (Listing 5). A knight class derives from the main object, and initialises its default state in the constructor:

The issue of artificial neural networks and their applications in video games has become one of the trendiest topics of recent days in the field of computer games. A lot has been said for years about their potential applications in computer games, in many magazines and on many Web portals. The neural networks in computer games problem has also been discussed, multiple times, at the GDC (Game Developers Conference an annual event taking place in London and San Jose). At the same time, we had to wait long to see a game enter the market whose engine would run based, at least minimally, on the potential of the artificial neural network theory.

The game Collin McRae Rally 2 is one of the first applications of neural networks in computer games, which became a total success. The trained artificial neural network is responsible for keeping the computer player's car on track while letting it negotiate the track as quickly as possible. In that game, just like I described in the AI in Sports Games section, each track is represented by a set of broken lines making up a graph. In a gross simplification, the neural network's input parameters are information such as: curvature of the road's bend, distance from the bend, type of surface, speed, or the vehicle's properties. It is up to the neural network to generate output data to be passed further to the physical layer module, that data being selected in such a way that the car travels and negotiates obstacles or curves at a speed optimal for the given conditions. Thanks to this, the computer player's driving style appears, contrary to other games of this kind, highly natural. The computer can avoid small obstacles, cut bends, begin turning appropriately soon when on a slippery surface etc. The game uses the multi-layered perceptron model, the simplified form of which one can see in Figure 6.

Figure 6. The multi-layered perceptron model

Artificial neural networks could, in theory, be applied to solving most tasks performed by AI in computer games. Unfortunately, in practice, a number of obstacles exist which limit the neural networks' application in games. These include:

What steps do we need to undertake in order to take advantage of an artificial neural network in a simple computer game? Let us have a brief look:

To begin, we have to answer our own question about the kinds of information the neural network should provide us with in order to help us solve the given problem. For example, let us consider a game in which a neural network controls the flight of our opponent's fighter plane. The information we should be obtaining from the neural network would then be, e.g., the optimal vectors of velocity and acceleration which, when provided to the physics module, will guide the enemy fighter to our plane. Another example could be a neural network used to choose the best strategy in a RTS-type game. Based on situation analysis, the network decides how greatly to concentrate on development, arms production, repairs after battles etc. All the parameters required by the game will be provided by the neural network on its output.

While defining the effect of the neural network's actions is quite easy (since we know exactly what we want to achieve), choosing the network's input parameters is a much more serious problem. The parameters should be chosen in such a way that its different combinations will let the neural network learn to solve complicated situations which haven't appeared in the example set of signals. The general rule states that the input data (variables) should represent as much information about the game world as possible; it could be, for instance, vectors of relative positions of the nearest obstacle and the nearest opponent, the enemy's strength, or the present state of armaments and damage.

Another step is to acquire a set of input data which will be used to train the network. The direct method could imply, e.g., remembering several to several hundred samples, successful attacks, and actions of a human player, and providing the recorded data to the neural network. Typically, however, the process used is automated, i.e., the samples themselves are computer-generated which requires an additional, often quite significant, effort from the programmers.

The final step is training the neural network. Any training algorithm can be used here. The training process should be interwoven with simultaneous testing in order to make sure the game is not becoming too difficult or, the opposite, if it's not still too easy and in need of further training and optimisation.

Applying neural networks practically is not an easy task. It requires a lot of time, experience, and patience. In addition, neural networks are often used together with fuzzy logic, which makes it possible to convert the computer's traditional zero-one reasoning into something more strongly resembling the way a human thinks. Logic lets us decide if and to what degree the given statement is true. Although simultaneous use of the two technologies is a difficult task, when it is successful, the results are simply breath-taking, and incomparable with what we can achieve by using rules hard-coded into the code with algorithms and traditional logic. Technologies such as neural networks, genetic algorithms, and fuzzy logic are the future of computer games and a future that is not that distant any more.

Developing an advanced artificial intelligence engine requires both time and an experienced team of programmers. If a development studio cannot allocate enough human resources to build an artificial intelligence system, it has a possibility of purchasing an existing AI system, many of which are available in the market. Here, I would like to provide a detailed description of one of the most popular libraries in the market Renderware AI, as well as of one of the newer libraries there, which could become a less expensive alternative to Renderware AI AI.Implant.

Renderware is a commercial, multiplatform computer game engine. The Renderware engine consists of several modules; among them, the one of interest to us here the Renderware AI artificial intelligence module.

The Renderware module can be used both in games wholly based on the Renderware engine, and in games which use their own or other engines, merely willing to make use of the Renderware AI as a basis for an advanced artificial intelligence system.

The Renderware AI library follows the layered philosophy of building artificial intelligence systems. Renderware AI discerns three layers:

The most important element of the whole library is representing the perception of the world, as this is what further layers of the game's AI base on. In Renderware AI, this module is called PathData (a slightly misleading name, considering path analysis is only one of the perception module's functions), and uses the tool called PathData Generator. The PathData module can successfully analyse the game world with respect to its topological properties, with the streaming method it features, making it possible to generate information required for the AI module even for very large game maps. PathData conducts both a global analysis of the terrain's topology and an analysis of the unit's nearest surroundings. The results of the analysis can then be, if such a need arises, subject to further manual processing.

Global analysis provides such information as the place on the map interesting from the point of view of its topological properties. This information can include data about: well-hidden locations on the map, locations from which large areas of the map can be seen well, where a camera could be placed so that its view won't be obscured by a minor element of the scene etc. Local analysis can let us detect walls, obstacles which have to be walked around or jumped over, and a lot of other locally important elements.

Renderware AI's another important feature is the module responsible for the function of widely understood planning and the execution of the movement of units. Using data provided by the world analysis module, an appropriate graph is built, which is then used by the A* algorithm to preliminary plan the optimal path from point A to point B. Other features include unit type-dependent paths, path smoothing, avoiding dynamic objects getting into the unit's way, coordination with the animation system, and many others, extremely important in practice.

The engine is available for many platforms, from Sony Playstation through Nintendo or XBox, to Sony PlayStation 2 and PCs. The libraries are optimised for each platform, and make it possible to create incredibly advanced AI systems. It is worth considering as an alternative to time-consuming development of one's own solutions for the field of artificial intelligence.

This engine, demonstrated for the first time in 2002 at the Game Developers Conference, immediately peaked the wide interest of computer game developers. The most important features of this system include advanced, hierarchic algorithms of path planning, a decision module based on binary decision trees and a friendly user interface enabling their edition. In addition, one of its great advantages is close integration with such programs as 3DStudio Max and Maya, which allows intuitive manipulation of data controlling object behaviour, as early as at the stage of their development in graphical packages. Among many other properties of the AI.Implant package, one worth mentioning is an advanced group behaviour module, making it possible to very realistically simulate crowds. AI.Implant is a multiplatform package available for PC, GameCube, XBox, and Sony PlayStation architectures.

Artificial intelligence is a very broad and, at the same time, fascinating part of computer science. In this article, I have introduced the reader to certain algorithms and methods of artificial intelligence used in programming computer games; however, it is only a small fragment of the knowledge any real computer game programmer must master. The most important issues not having been discussed here include: genetic programming, fuzzy logic, impact map method, flock algorithms, and many, many others; getting familiar with these, I heartily recommend to all the readers. At the end, I'm providing a list of books and Web page references which can be useful to anyone who would like to single-handedly increase one's knowledge of the field of artificial intelligence in computer games.

More here:

Artificial Intelligence in Games - CodeProject