Daily Archives: August 30, 2016

In video games, artificial intelligence is used to generate intelligent behaviors primarily in non-player characters (NPCs), often simulating human-like intelligence. The techniques used typically draw upon existing methods from the field of artificial intelligence (AI). However, the term game AI is often used to refer to a broad set of algorithms that also include techniques from control theory, robotics, computer graphics and computer science in general.

Since game AI for NPCs is centered on appearance of intelligence and good gameplay within environment restrictions, its approach is very different from that of traditional AI; workarounds and cheats are acceptable and, in many cases, the computer abilities must be toned down to give human players a sense of fairness. This, for example, is true in first-person shooter games, where NPCs' otherwise perfect aiming would be beyond human skill.

Game playing was an area of research in AI from its inception. One of the first examples of AI is the computerised game of Nim made in 1951 and published in 1952. Despite being advanced technology in the year it was made, 20 years before Pong, the game took the form of a relatively small box and was able to regularly win games even against highly skilled players of the game.[1] In 1951, using the Ferranti Mark 1 machine of the University of Manchester, Christopher Strachey wrote a checkers program and Dietrich Prinz wrote one for chess.[2] These were among the first computer programs ever written. Arthur Samuel's checkers program, developed in the middle 50s and early 60s, eventually achieved sufficient skill to challenge a respectable amateur.[3] Work on checkers and chess would culminate in the defeat of Garry Kasparov by IBM's Deep Blue computer in 1997.[4] The first video games developed in the 1960s and early 1970s, like Spacewar!, Pong, and Gotcha (1973), were games implemented on discrete logic and strictly based on the competition of two players, without AI.

Games that featured a single player mode with enemies started appearing in the 1970s. The first notable ones for the arcade appeared in 1974: the Taito game Speed Race (racing video game) and the Atari games Qwak (duck hunting light gun shooter) and Pursuit (fighter aircraft dogfighting simulator). Two text-based computer games from 1972, Hunt the Wumpus and Star Trek, also had enemies. Enemy movement was based on stored patterns. The incorporation of microprocessors would allow more computation and random elements overlaid into movement patterns.

It was during the golden age of video arcade games that the idea of AI opponents was largely popularized, due to the success of Space Invaders (1978), which sported an increasing difficulty level, distinct movement patterns, and in-game events dependent on hash functions based on the player's input. Galaxian (1979) added more complex and varied enemy movements, including maneuvers by individual enemies who break out of formation. Pac-Man (1980) introduced AI patterns to maze games, with the added quirk of different personalities for each enemy. Karate Champ (1984) later introduced AI patterns to fighting games, although the poor AI prompted the release of a second version. First Queen (1988) was a tactical action RPG which featured characters that can be controlled by the computer's AI in following the leader.[5][6] The role-playing video game Dragon Quest IV (1990) introduced a "Tactics" system, where the user can adjust the AI routines of non-player characters during battle, a concept later introduced to the action role-playing game genre by Secret of Mana (1993).

Games like Madden Football, Earl Weaver Baseball and Tony La Russa Baseball all based their AI on an attempt to duplicate on the computer the coaching or managerial style of the selected celebrity. Madden, Weaver and La Russa all did extensive work with these game development teams to maximize the accuracy of the games.[citation needed] Later sports titles allowed users to "tune" variables in the AI to produce a player-defined managerial or coaching strategy.

The emergence of new game genres in the 1990s prompted the use of formal AI tools like finite state machines. Real-time strategy games taxed the AI with many objects, incomplete information, pathfinding problems, real-time decisions and economic planning, among other things.[7] The first games of the genre had notorious problems. Herzog Zwei (1989), for example, had almost broken pathfinding and very basic three-state state machines for unit control, and Dune II (1992) attacked the players' base in a beeline and used numerous cheats.[8] Later games in the genre exhibited more sophisticated AI.

Later games have used bottom-up AI methods, such as the emergent behaviour and evaluation of player actions in games like Creatures or Black & White. Faade (interactive story) was released in 2005 and used interactive multiple way dialogs and AI as the main aspect of game.

Games have provided an environment for developing artificial intelligence with potential applications beyond gameplay. Examples include Watson, a Jeopardy!-playing computer; and the RoboCup tournament, where robots are trained to compete in soccer.[9]

Purists complain that the "AI" in the term "game AI" overstates its worth, as game AI is not about intelligence, and shares few of the objectives of the academic field of AI. Whereas "real" AI addresses fields of machine learning, decision making based on arbitrary data input, and even the ultimate goal of strong AI that can reason, "game AI" often consists of a half-dozen rules of thumb, or heuristics, that are just enough to give a good gameplay experience.[citation needed] Historically, academic game-AI projects have been relatively separate from commercial products because the academic approaches tended to be simple and non-scalable. Commercial game AI has developed its own set of tools, which have been sufficient to give good performance in many cases.[10]

Game developers' increasing awareness of academic AI and a growing interest in computer games by the academic community is causing the definition of what counts as AI in a game to become less idiosyncratic. Nevertheless, significant differences between different application domains of AI mean that game AI can still be viewed as a distinct subfield of AI. In particular, the ability to legitimately solve some AI problems in games by cheating creates an important distinction. For example, inferring the position of an unseen object from past observations can be a difficult problem when AI is applied to robotics, but in a computer game a NPC can simply look up the position in the game's scene graph. Such cheating can lead to unrealistic behavior and so is not always desirable. But its possibility serves to distinguish game AI and leads to new problems to solve, such as when and how to use cheating.[citation needed]

The major limitation to strong AI is the inherent depth of thinking and the extreme complexity of the decision making process. This means that although it would be then theoretically possible to make "smart" AI the problem would take considerable processing power.[citation needed]

Game AI/heuristic algorithms are used in a wide variety of quite disparate fields inside a game. The most obvious is in the control of any NPCs in the game, although scripting is currently the most common means of control. Pathfinding is another common use for AI, widely seen in real-time strategy games. Pathfinding is the method for determining how to get a NPC from one point on a map to another, taking into consideration the terrain, obstacles and possibly "fog of war". Beyond pathfinding, navigation is a sub-field of Game AI focusing on giving NPCs the capability to navigate in their environment, finding a path to a target while avoiding collisions with other entities (other NPC, players...) or collaborating with them (group navigation).

The concept of emergent AI has recently been explored in games such as Creatures, Black & White and Nintendogs and toys such as Tamagotchi. The "pets" in these games are able to "learn" from actions taken by the player and their behavior is modified accordingly. While these choices are taken from a limited pool, it does often give the desired illusion of an intelligence on the other side of the screen.

Many contemporary video games fall under the category of action, first person shooter, or adventure. In most of these types of games there is some level of combat that takes place. The AI's ability to be efficient in combat is important in these genres. A common goal today is to make the AI more human, or at least appear so.

One of the more positive and efficient features found in modern-day video game AI is the ability to hunt. AI originally reacted in a very black and white manner. If the player were in a specific area then the AI would react in either a complete offensive manner or be entirely defensive. In recent years, the idea of "hunting" has been introduced; in this 'hunting' state the AI will look for realistic markers, such as sounds made by the character or footprints they may have left behind.[11] These developments ultimately allow for a more complex form of play. With this feature, the player can actually consider how to approach or avoid an enemy. This is a feature that is particularly prevalent in the stealth genre.

Another development in recent game AI has been the development of "survival instinct". In-game computers can recognize different objects in an environment and determine whether it is beneficial or detrimental to its survival. Like a user, the AI can "look" for cover in a firefight before taking actions that would leave it otherwise vulnerable, such as reloading a weapon or throwing a grenade. There can be set markers that tell it when to react in a certain way. For example, if the AI is given a command to check its health throughout a game then further commands can be set so that it reacts a specific way at a certain percentage of health. If the health is below a certain threshold then the AI can be set to run away from the player and avoid it until another function is triggered. Another example could be if the AI notices it is out of bullets, it will find a cover object and hide behind it until it has reloaded. Actions like these make the AI seem more human. However, there is still a need for improvement in this area.

Another side-effect of combat AI occurs when two AI-controlled characters encounter each other; first popularized in the id Software game Doom, so-called 'monster infighting' can break out in certain situations. Specifically, AI agents that are programmed to respond to hostile attacks will sometimes attack each other if their cohort's attacks land too close to them.[citation needed] In the case of Doom, published gameplay manuals even suggest taking advantage of monster infighting in order to survive certain levels and difficulty settings.

Georgios N. Yannakakis suggests that academic AI developments can play roles in game AI beyond the traditional paradigm of AI controlling NPC behavior.[10] He highlights four other potential application areas:

In the context of artificial intelligence in video games, cheating refers to the programmer giving agents actions and access to information that would be unavailable to the player in the same situation.[12] In a simple example, if the agents want to know if the player is nearby they can either be given complex, human-like sensors (seeing, hearing, etc.), or they can cheat by simply asking the game engine for the player's position. Common variations include giving AIs higher speeds in racing games to catch up to the player or spawning them in advantageous positions in first person shooters. The use of cheating in AI shows the limitations of the "intelligence" achievable artificially; generally speaking, in games where strategic creativity is important, humans could easily beat the AI after a minimum of trial and error if it were not for this advantage. Cheating is often implemented for performance reasons where in many cases it may be considered acceptable as long as the effect is not obvious to the player. While cheating refers only to privileges given specifically to the AIit does not include the inhuman swiftness and precision natural to a computera player might call the computer's inherent advantages "cheating" if they result in the agent acting unlike a human player.[12]Sid Meier stated that he omitted multiplayer alliances in Civilization because he found that the computer was almost as good as humans in using them, which caused players to think that the computer was cheating.[13]

Creatures is an artificial life program where the user "hatches" small furry animals and teaches them how to behave. These "Norns" can talk, feed themselves, and protect themselves against vicious creatures. It's the first popular application of machine learning into an interactive simulation. Neural networks are used by the creatures to learn what to do. The game is regarded as a breakthrough in artificial life research, which aims to model the behavior of creatures interacting with their environment.[14]

A first-person shooter where the player assumes the role of the Master Chief, battling various aliens on foot or in vehicles. Enemies use cover very wisely, and employ suppressive fire and grenades. The squad situation affects the individuals, so certain enemies flee when their leader dies. A lot of attention is paid to the little details, with enemies notably throwing back grenades or team-members responding to you bothering them. The underlying "behavior tree" technology has become very popular in the games industry (especially since Halo 2).[14]

A first-person shooter where the player helps contain supernatural phenomenon and armies of cloned soldiers. The AI uses a planner to generate context-sensitive behaviors, the first time in a mainstream game. This technology used as a reference for many studios still today. The enemies are capable of using the environment very cleverly, finding cover behind tables, tipping bookshelves, opening doors, crashing through windows, and so on. Squad tactics are used to great effect. The enemies perform flanking maneuvers, use suppression fire, etc.[14]

A first-person shooter survival horror game where the player must face man-made experiments, military soldiers, and mercenaries known as Stalkers. The various encountered enemies (if the difficulty level is set to its highest) use combat tactics and behaviours such as healing wounded allies, giving orders, out-flanking the player or using weapons with pinpoint accuracy.[citation needed]

A first-person shooter where the player fights off numerous mercenaries and assassinates faction leaders. The AI is behavior based and uses action selection, essential if an AI is to multitask or react to a situation. The AI can react in an unpredictable fashion in many situations. The enemies respond to sounds and visual distractions such as fire or nearby explosions and can be subject to investigate the hazard, the player can utilize these distractions to his own advantage. There are also social interfaces with an AI but however not in the form of direct conversation but more reactionary, if the player gets too close or even nudges an AI, the player is subject to getting shoved off or sworn at and by extent getting aimed at. Other social interfaces between AI exist when in combat, or neutral situations, if an enemy AI is injured on the ground, he will shout out for help, release emotional distress, etc.[citation needed]

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Artificial intelligence (video games) - Wikipedia, the free ...

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with "an ancient wish to forge the gods."

The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.

The Turing test was proposed by British mathematician Alan Turing in his 1950 paper Computing Machinery and Intelligence, which opens with the words: "I propose to consider the question, 'Can machines think?'" The term 'Artificial Intelligence' was created at a conference held at Dartmouth College in 1956.[2]Allen Newell, J. C. Shaw, and Herbert A. Simon pioneered the newly created artificial intelligence field with the Logic Theory Machine (1956), and the General Problem Solver in 1957.[3] In 1958, John McCarthy and Marvin Minsky started the MIT Artificial Intelligence lab with $50,000.[4] John McCarthy also created LISP in the summer of 1958, a programming language still important in artificial intelligence research.[5]

In 1973, in response to the criticism of James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence. Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned and withdrew funding again.

McCorduck (2004) writes "artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized," expressed in humanity's myths, legends, stories, speculation and clockwork automatons.

Mechanical men and artificial beings appear in Greek myths, such as the golden robots of Hephaestus and Pygmalion's Galatea.[7] In the Middle Ages, there were rumors of secret mystical or alchemical means of placing mind into matter, such as Jbir ibn Hayyn's Takwin, Paracelsus' homunculus and Rabbi Judah Loew's Golem.[8] By the 19th century, ideas about artificial men and thinking machines were developed in fiction, as in Mary Shelley's Frankenstein or Karel apek's R.U.R. (Rossum's Universal Robots), and speculation, such as Samuel Butler's "Darwin among the Machines." AI has continued to be an important element of science fiction into the present.

Realistic humanoid automatons were built by craftsman from every civilization, including Yan Shi,[11]Hero of Alexandria,[12]Al-Jazari and Wolfgang von Kempelen.[14] The oldest known automatons were the sacred statues of ancient Egypt and Greece. The faithful believed that craftsman had imbued these figures with very real minds, capable of wisdom and emotionHermes Trismegistus wrote that "by discovering the true nature of the gods, man has been able to reproduce it."[15][16]

Artificial intelligence is based on the assumption that the process of human thought can be mechanized. The study of mechanicalor "formal"reasoning has a long history. Chinese, Indian and Greek philosophers all developed structured methods of formal deduction in the first millennium BCE. Their ideas were developed over the centuries by philosophers such as Aristotle (who gave a formal analysis of the syllogism), Euclid (whose Elements was a model of formal reasoning), Muslim mathematician al-Khwrizm (who developed algebra and gave his name to "algorithm") and European scholastic philosophers such as William of Ockham and Duns Scotus.[17]

Majorcan philosopher Ramon Llull (12321315) developed several logical machines devoted to the production of knowledge by logical means;[18] Llull described his machines as mechanical entities that could combine basic and undeniable truths by simple logical operations, produced by the machine by mechanical meanings, in such ways as to produce all the possible knowledge.[19] Llull's work had a great influence on Gottfried Leibniz, who redeveloped his ideas.[20]

In the 17th century, Leibniz, Thomas Hobbes and Ren Descartes explored the possibility that all rational thought could be made as systematic as algebra or geometry.[21]Hobbes famously wrote in Leviathan: "reason is nothing but reckoning".[22]Leibniz envisioned a universal language of reasoning (his characteristica universalis) which would reduce argumentation to calculation, so that "there would be no more need of disputation between two philosophers than between two accountants. For it would suffice to take their pencils in hand, down to their slates, and to say each other (with a friend as witness, if they liked): Let us calculate."[23] These philosophers had begun to articulate the physical symbol system hypothesis that would become the guiding faith of AI research.

In the 20th century, the study of mathematical logic provided the essential breakthrough that made artificial intelligence seem plausible. The foundations had been set by such works as Boole's The Laws of Thought and Frege's Begriffsschrift. Building on Frege's system, Russell and Whitehead presented a formal treatment of the foundations of mathematics in their masterpiece, the Principia Mathematica in 1913. Inspired by Russell's success, David Hilbert challenged mathematicians of the 1920s and 30s to answer this fundamental question: "can all of mathematical reasoning be formalized?"[17] His question was answered by Gdel's incompleteness proof, Turing's machine and Church's Lambda calculus.[17][24] Their answer was surprising in two ways.

First, they proved that there were, in fact, limits to what mathematical logic could accomplish. But second (and more important for AI) their work suggested that, within these limits, any form of mathematical reasoning could be mechanized. The Church-Turing thesis implied that a mechanical device, shuffling symbols as simple as 0 and 1, could imitate any conceivable process of mathematical deduction. The key insight was the Turing machinea simple theoretical construct that captured the essence of abstract symbol manipulation. This invention would inspire a handful of scientists to begin discussing the possibility of thinking machines.[17][26]

Calculating machines were built in antiquity and improved throughout history by many mathematicians, including (once again) philosopher Gottfried Leibniz. In the early 19th century, Charles Babbage designed a programmable computer (the Analytical Engine), although it was never built. Ada Lovelace speculated that the machine "might compose elaborate and scientific pieces of music of any degree of complexity or extent".[27] (She is often credited as the first programmer because of a set of notes she wrote that completely detail a method for calculating Bernoulli numbers with the Engine.)

The first modern computers were the massive code breaking machines of the Second World War (such as Z3, ENIAC and Colossus). The latter two of these machines were based on the theoretical foundation laid by Alan Turing[28] and developed by John von Neumann.[29]

In the 1940s and 50s, a handful of scientists from a variety of fields (mathematics, psychology, engineering, economics and political science) began to discuss the possibility of creating an artificial brain. The field of artificial intelligence research was founded as an academic discipline in 1956.

The earliest research into thinking machines was inspired by a confluence of ideas that became prevalent in the late 30s, 40s and early 50s. Recent research in neurology had shown that the brain was an electrical network of neurons that fired in all-or-nothing pulses. Norbert Wiener's cybernetics described control and stability in electrical networks. Claude Shannon's information theory described digital signals (i.e., all-or-nothing signals). Alan Turing's theory of computation showed that any form of computation could be described digitally. The close relationship between these ideas suggested that it might be possible to construct an electronic brain.[30]

Examples of work in this vein includes robots such as W. Grey Walter's turtles and the Johns Hopkins Beast. These machines did not use computers, digital electronics or symbolic reasoning; they were controlled entirely by analog circuitry.[31]

Walter Pitts and Warren McCulloch analyzed networks of idealized artificial neurons and showed how they might perform simple logical functions. They were the first to describe what later researchers would call a neural network.[32] One of the students inspired by Pitts and McCulloch was a young Marvin Minsky, then a 24-year-old graduate student. In 1951 (with Dean Edmonds) he built the first neural net machine, the SNARC.[33]Minsky was to become one of the most important leaders and innovators in AI for the next 50 years.

In 1950 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think.[34] He noted that "thinking" is difficult to define and devised his famous Turing Test. If a machine could carry on a conversation (over a teleprinter) that was indistinguishable from a conversation with a human being, then it was reasonable to say that the machine was "thinking". This simplified version of the problem allowed Turing to argue convincingly that a "thinking machine" was at least plausible and the paper answered all the most common objections to the proposition.[35] The Turing Test was the first serious proposal in the philosophy of artificial intelligence.

In 1951, using the Ferranti Mark 1 machine of the University of Manchester, Christopher Strachey wrote a checkers program and Dietrich Prinz wrote one for chess.[36]Arthur Samuel's checkers program, developed in the middle 50s and early 60s, eventually achieved sufficient skill to challenge a respectable amateur.[37]Game AI would continue to be used as a measure of progress in AI throughout its history.

When access to digital computers became possible in the middle fifties, a few scientists instinctively recognized that a machine that could manipulate numbers could also manipulate symbols and that the manipulation of symbols could well be the essence of human thought. This was a new approach to creating thinking machines.[38]

In 1955, Allen Newell and (future Nobel Laureate) Herbert A. Simon created the "Logic Theorist" (with help from J. C. Shaw). The program would eventually prove 38 of the first 52 theorems in Russell and Whitehead's Principia Mathematica, and find new and more elegant proofs for some.[39] Simon said that they had "solved the venerable mind/body problem, explaining how a system composed of matter can have the properties of mind."[40] (This was an early statement of the philosophical position John Searle would later call "Strong AI": that machines can contain minds just as human bodies do.)[41]

The Dartmouth Conference of 1956[42] was organized by Marvin Minsky, John McCarthy and two senior scientists: Claude Shannon and Nathan Rochester of IBM. The proposal for the conference included this assertion: "every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it".[43] The participants included Ray Solomonoff, Oliver Selfridge, Trenchard More, Arthur Samuel, Allen Newell and Herbert A. Simon, all of whom would create important programs during the first decades of AI research.[44] At the conference Newell and Simon debuted the "Logic Theorist" and McCarthy persuaded the attendees to accept "Artificial Intelligence" as the name of the field.[45] The 1956 Dartmouth conference was the moment that AI gained its name, its mission, its first success and its major players, and is widely considered the birth of AI.[46]

The years after the Dartmouth conference were an era of discovery, of sprinting across new ground. The programs that were developed during this time were, to most people, simply "astonishing":[47] computers were solving algebra word problems, proving theorems in geometry and learning to speak English. Few at the time would have believed that such "intelligent" behavior by machines was possible at all.[48] Researchers expressed an intense optimism in private and in print, predicting that a fully intelligent machine would be built in less than 20 years.[49] Government agencies like ARPA poured money into the new field.[50]

There were many successful programs and new directions in the late 50s and 1960s. Among the most influential were these:

Many early AI programs used the same basic algorithm. To achieve some goal (like winning a game or proving a theorem), they proceeded step by step towards it (by making a move or a deduction) as if searching through a maze, backtracking whenever they reached a dead end. This paradigm was called "reasoning as search".[51]

The principal difficulty was that, for many problems, the number of possible paths through the "maze" was simply astronomical (a situation known as a "combinatorial explosion"). Researchers would reduce the search space by using heuristics or "rules of thumb" that would eliminate those paths that were unlikely to lead to a solution.[52]

Newell and Simon tried to capture a general version of this algorithm in a program called the "General Problem Solver".[53] Other "searching" programs were able to accomplish impressive tasks like solving problems in geometry and algebra, such as Herbert Gelernter's Geometry Theorem Prover (1958) and SAINT, written by Minsky's student James Slagle (1961).[54] Other programs searched through goals and subgoals to plan actions, like the STRIPS system developed at Stanford to control the behavior of their robot Shakey.[55]

An important goal of AI research is to allow computers to communicate in natural languages like English. An early success was Daniel Bobrow's program STUDENT, which could solve high school algebra word problems.[56]

A semantic net represents concepts (e.g. "house","door") as nodes and relations among concepts (e.g. "has-a") as links between the nodes. The first AI program to use a semantic net was written by Ross Quillian[57] and the most successful (and controversial) version was Roger Schank's Conceptual dependency theory.[58]

Joseph Weizenbaum's ELIZA could carry out conversations that were so realistic that users occasionally were fooled into thinking they were communicating with a human being and not a program. But in fact, ELIZA had no idea what she was talking about. She simply gave a canned response or repeated back what was said to her, rephrasing her response with a few grammar rules. ELIZA was the first chatterbot.[59]

In the late 60s, Marvin Minsky and Seymour Papert of the MIT AI Laboratory proposed that AI research should focus on artificially simple situations known as micro-worlds. They pointed out that in successful sciences like physics, basic principles were often best understood using simplified models like frictionless planes or perfectly rigid bodies. Much of the research focused on a "blocks world," which consists of colored blocks of various shapes and sizes arrayed on a flat surface.[60]

This paradigm led to innovative work in machine vision by Gerald Sussman (who led the team), Adolfo Guzman, David Waltz (who invented "constraint propagation"), and especially Patrick Winston. At the same time, Minsky and Papert built a robot arm that could stack blocks, bringing the blocks world to life. The crowning achievement of the micro-world program was Terry Winograd's SHRDLU. It could communicate in ordinary English sentences, plan operations and execute them.[61]

The first generation of AI researchers made these predictions about their work:

In June 1963, MIT received a $2.2 million grant from the newly created Advanced Research Projects Agency (later known as DARPA). The money was used to fund project MAC which subsumed the "AI Group" founded by Minsky and McCarthy five years earlier. DARPA continued to provide three million dollars a year until the 70s.[66]DARPA made similar grants to Newell and Simon's program at CMU and to the Stanford AI Project (founded by John McCarthy in 1963).[67] Another important AI laboratory was established at Edinburgh University by Donald Michie in 1965.[68] These four institutions would continue to be the main centers of AI research (and funding) in academia for many years.[69]

The money was proffered with few strings attached: J. C. R. Licklider, then the director of ARPA, believed that his organization should "fund people, not projects!" and allowed researchers to pursue whatever directions might interest them.[70] This created a freewheeling atmosphere at MIT that gave birth to the hacker culture,[71] but this "hands off" approach would not last.

In the 70s, AI was subject to critiques and financial setbacks. AI researchers had failed to appreciate the difficulty of the problems they faced. Their tremendous optimism had raised expectations impossibly high, and when the promised results failed to materialize, funding for AI disappeared.[72] At the same time, the field of connectionism (or neural nets) was shut down almost completely for 10 years by Marvin Minsky's devastating criticism of perceptrons.[73] Despite the difficulties with public perception of AI in the late 70s, new ideas were explored in logic programming, commonsense reasoning and many other areas.[74]

In the early seventies, the capabilities of AI programs were limited. Even the most impressive could only handle trivial versions of the problems they were supposed to solve; all the programs were, in some sense, "toys".[75] AI researchers had begun to run into several fundamental limits that could not be overcome in the 1970s. Although some of these limits would be conquered in later decades, others still stymie the field to this day.[76]

The agencies which funded AI research (such as the British government, DARPA and NRC) became frustrated with the lack of progress and eventually cut off almost all funding for undirected research into AI. The pattern began as early as 1966 when the ALPAC report appeared criticizing machine translation efforts. After spending 20 million dollars, the NRC ended all support.[84] In 1973, the Lighthill report on the state of AI research in England criticized the utter failure of AI to achieve its "grandiose objectives" and led to the dismantling of AI research in that country.[85] (The report specifically mentioned the combinatorial explosion problem as a reason for AI's failings.)[86]DARPA was deeply disappointed with researchers working on the Speech Understanding Research program at CMU and canceled an annual grant of three million dollars.[87] By 1974, funding for AI projects was hard to find.

Hans Moravec blamed the crisis on the unrealistic predictions of his colleagues. "Many researchers were caught up in a web of increasing exaggeration."[88] However, there was another issue: since the passage of the Mansfield Amendment in 1969, DARPA had been under increasing pressure to fund "mission-oriented direct research, rather than basic undirected research". Funding for the creative, freewheeling exploration that had gone on in the 60s would not come from DARPA. Instead, the money was directed at specific projects with clear objectives, such as autonomous tanks and battle management systems.[89]

Several philosophers had strong objections to the claims being made by AI researchers. One of the earliest was John Lucas, who argued that Gdel's incompleteness theorem showed that a formal system (such as a computer program) could never see the truth of certain statements, while a human being could.[90]Hubert Dreyfus ridiculed the broken promises of the 60s and critiqued the assumptions of AI, arguing that human reasoning actually involved very little "symbol processing" and a great deal of embodied, instinctive, unconscious "know how".[91][92]John Searle's Chinese Room argument, presented in 1980, attempted to show that a program could not be said to "understand" the symbols that it uses (a quality called "intentionality"). If the symbols have no meaning for the machine, Searle argued, then the machine can not be described as "thinking".[93]

These critiques were not taken seriously by AI researchers, often because they seemed so far off the point. Problems like intractability and commonsense knowledge seemed much more immediate and serious. It was unclear what difference "know how" or "intentionality" made to an actual computer program. Minsky said of Dreyfus and Searle "they misunderstand, and should be ignored."[94] Dreyfus, who taught at MIT, was given a cold shoulder: he later said that AI researchers "dared not be seen having lunch with me."[95]Joseph Weizenbaum, the author of ELIZA, felt his colleagues' treatment of Dreyfus was unprofessional and childish. Although he was an outspoken critic of Dreyfus' positions, he "deliberately made it plain that theirs was not the way to treat a human being."[96]

Weizenbaum began to have serious ethical doubts about AI when Kenneth Colby wrote DOCTOR, a chatterbot therapist. Weizenbaum was disturbed that Colby saw his mindless program as a serious therapeutic tool. A feud began, and the situation was not helped when Colby did not credit Weizenbaum for his contribution to the program. In 1976, Weizenbaum published Computer Power and Human Reason which argued that the misuse of artificial intelligence has the potential to devalue human life.[97]

A perceptron was a form of neural network introduced in 1958 by Frank Rosenblatt, who had been a schoolmate of Marvin Minsky at the Bronx High School of Science. Like most AI researchers, he was optimistic about their power, predicting that "perceptron may eventually be able to learn, make decisions, and translate languages." An active research program into the paradigm was carried out throughout the 60s but came to a sudden halt with the publication of Minsky and Papert's 1969 book Perceptrons. It suggested that there were severe limitations to what perceptrons could do and that Frank Rosenblatt's predictions had been grossly exaggerated. The effect of the book was devastating: virtually no research at all was done in connectionism for 10 years. Eventually, a new generation of researchers would revive the field and thereafter it would become a vital and useful part of artificial intelligence. Rosenblatt would not live to see this, as he died in a boating accident shortly after the book was published.[73]

Logic was introduced into AI research as early as 1958, by John McCarthy in his Advice Taker proposal.[98] In 1963, J. Alan Robinson had discovered a simple method to implement deduction on computers, the resolution and unification algorithm. However, straightforward implementations, like those attempted by McCarthy and his students in the late 60s, were especially intractable: the programs required astronomical numbers of steps to prove simple theorems.[99] A more fruitful approach to logic was developed in the 1970s by Robert Kowalski at the University of Edinburgh, and soon this led to the collaboration with French researchers Alain Colmerauer and Philippe Roussel who created the successful logic programming language Prolog.[100] Prolog uses a subset of logic (Horn clauses, closely related to "rules" and "production rules") that permit tractable computation. Rules would continue to be influential, providing a foundation for Edward Feigenbaum's expert systems and the continuing work by Allen Newell and Herbert A. Simon that would lead to Soar and their unified theories of cognition.[101]

Critics of the logical approach noted, as Dreyfus had, that human beings rarely used logic when they solved problems. Experiments by psychologists like Peter Wason, Eleanor Rosch, Amos Tversky, Daniel Kahneman and others provided proof.[102] McCarthy responded that what people do is irrelevant. He argued that what is really needed are machines that can solve problemsnot machines that think as people do.[103]

Among the critics of McCarthy's approach were his colleagues across the country at MIT. Marvin Minsky, Seymour Papert and Roger Schank were trying to solve problems like "story understanding" and "object recognition" that required a machine to think like a person. In order to use ordinary concepts like "chair" or "restaurant" they had to make all the same illogical assumptions that people normally made. Unfortunately, imprecise concepts like these are hard to represent in logic. Gerald Sussman observed that "using precise language to describe essentially imprecise concepts doesn't make them any more precise."[104]Schank described their "anti-logic" approaches as "scruffy", as opposed to the "neat" paradigms used by McCarthy, Kowalski, Feigenbaum, Newell and Simon.[105]

In 1975, in a seminal paper, Minsky noted that many of his fellow "scruffy" researchers were using the same kind of tool: a framework that captures all our common sense assumptions about something. For example, if we use the concept of a bird, there is a constellation of facts that immediately come to mind: we might assume that it flies, eats worms and so on. We know these facts are not always true and that deductions using these facts will not be "logical", but these structured sets of assumptions are part of the context of everything we say and think. He called these structures "frames". Schank used a version of frames he called "scripts" to successfully answer questions about short stories in English.[106] Many years later object-oriented programming would adopt the essential idea of "inheritance" from AI research on frames.

In the 1980s a form of AI program called "expert systems" was adopted by corporations around the world and knowledge became the focus of mainstream AI research. In those same years, the Japanese government aggressively funded AI with its fifth generation computer project. Another encouraging event in the early 1980s was the revival of connectionism in the work of John Hopfield and David Rumelhart. Once again, AI had achieved success.

An expert system is a program that answers questions or solves problems about a specific domain of knowledge, using logical rules that are derived from the knowledge of experts. The earliest examples were developed by Edward Feigenbaum and his students. Dendral, begun in 1965, identified compounds from spectrometer readings. MYCIN, developed in 1972, diagnosed infectious blood diseases. They demonstrated the feasibility of the approach.[107]

Expert systems restricted themselves to a small domain of specific knowledge (thus avoiding the commonsense knowledge problem) and their simple design made it relatively easy for programs to be built and then modified once they were in place. All in all, the programs proved to be useful: something that AI had not been able to achieve up to this point.[108]

In 1980, an expert system called XCON was completed at CMU for the Digital Equipment Corporation. It was an enormous success: it was saving the company 40 million dollars annually by 1986.[109] Corporations around the world began to develop and deploy expert systems and by 1985 they were spending over a billion dollars on AI, most of it to in-house AI departments. An industry grew up to support them, including hardware companies like Symbolics and Lisp Machines and software companies such as IntelliCorp and Aion.[110]

The power of expert systems came from the expert knowledge they contained. They were part of a new direction in AI research that had been gaining ground throughout the 70s. "AI researchers were beginning to suspectreluctantly, for it violated the scientific canon of parsimonythat intelligence might very well be based on the ability to use large amounts of diverse knowledge in different ways,"[111] writes Pamela McCorduck. "[T]he great lesson from the 1970s was that intelligent behavior depended very much on dealing with knowledge, sometimes quite detailed knowledge, of a domain where a given task lay".[112]Knowledge based systems and knowledge engineering became a major focus of AI research in the 1980s.[113]

The 1980s also saw the birth of Cyc, the first attempt to attack the commonsense knowledge problem directly, by creating a massive database that would contain all the mundane facts that the average person knows. Douglas Lenat, who started and led the project, argued that there is no shortcut the only way for machines to know the meaning of human concepts is to teach them, one concept at a time, by hand. The project was not expected to be completed for many decades.[114]

Chess playing programs HiTech and Deep Thought defeated chess masters in 1989. Both were developed by Carnegie Mellon University; Deep Thought development paved the way for the Deep Blue.[115]

In 1981, the Japanese Ministry of International Trade and Industry set aside $850 million for the Fifth generation computer project. Their objectives were to write programs and build machines that could carry on conversations, translate languages, interpret pictures, and reason like human beings.[116] Much to the chagrin of scruffies, they chose Prolog as the primary computer language for the project.[117]

Other countries responded with new programs of their own. The UK began the 350 million Alvey project. A consortium of American companies formed the Microelectronics and Computer Technology Corporation (or "MCC") to fund large scale projects in AI and information technology.[118][119]DARPA responded as well, founding the Strategic Computing Initiative and tripling its investment in AI between 1984 and 1988.[120]

In 1982, physicist John Hopfield was able to prove that a form of neural network (now called a "Hopfield net") could learn and process information in a completely new way. Around the same time, David Rumelhart popularized a new method for training neural networks called "backpropagation" (discovered years earlier by Paul Werbos). These two discoveries revived the field of connectionism which had been largely abandoned since 1970.[119][121]

The new field was unified and inspired by the appearance of Parallel Distributed Processing in 1986a two volume collection of papers edited by Rumelhart and psychologist James McClelland. Neural networks would become commercially successful in the 1990s, when they began to be used as the engines driving programs like optical character recognition and speech recognition.[119][122]

The business community's fascination with AI rose and fell in the 80s in the classic pattern of an economic bubble. The collapse was in the perception of AI by government agencies and investors the field continued to make advances despite the criticism. Rodney Brooks and Hans Moravec, researchers from the related field of robotics, argued for an entirely new approach to artificial intelligence.

The term "AI winter" was coined by researchers who had survived the funding cuts of 1974 when they became concerned that enthusiasm for expert systems had spiraled out of control and that disappointment would certainly follow.[123] Their fears were well founded: in the late 80s and early 90s, AI suffered a series of financial setbacks.

The first indication of a change in weather was the sudden collapse of the market for specialized AI hardware in 1987. Desktop computers from Apple and IBM had been steadily gaining speed and power and in 1987 they became more powerful than the more expensive Lisp machines made by Symbolics and others. There was no longer a good reason to buy them. An entire industry worth half a billion dollars was demolished overnight.[124]

Eventually the earliest successful expert systems, such as XCON, proved too expensive to maintain. They were difficult to update, they could not learn, they were "brittle" (i.e., they could make grotesque mistakes when given unusual inputs), and they fell prey to problems (such as the qualification problem) that had been identified years earlier. Expert systems proved useful, but only in a few special contexts.[125]

In the late 80s, the Strategic Computing Initiative cut funding to AI "deeply and brutally." New leadership at DARPA had decided that AI was not "the next wave" and directed funds towards projects that seemed more likely to produce immediate results.[126]

By 1991, the impressive list of goals penned in 1981 for Japan's Fifth Generation Project had not been met. Indeed, some of them, like "carry on a casual conversation" had not been met by 2010.[127] As with other AI projects, expectations had run much higher than what was actually possible.[127]

In the late 80s, several researchers advocated a completely new approach to artificial intelligence, based on robotics.[128] They believed that, to show real intelligence, a machine needs to have a body it needs to perceive, move, survive and deal with the world. They argued that these sensorimotor skills are essential to higher level skills like commonsense reasoning and that abstract reasoning was actually the least interesting or important human skill (see Moravec's paradox). They advocated building intelligence "from the bottom up."[129]

The approach revived ideas from cybernetics and control theory that had been unpopular since the sixties. Another precursor was David Marr, who had come to MIT in the late 70s from a successful background in theoretical neuroscience to lead the group studying vision. He rejected all symbolic approaches (both McCarthy's logic and Minsky's frames), arguing that AI needed to understand the physical machinery of vision from the bottom up before any symbolic processing took place. (Marr's work would be cut short by leukemia in 1980.)[130]

In a 1990 paper, "Elephants Don't Play Chess,"[131] robotics researcher Rodney Brooks took direct aim at the physical symbol system hypothesis, arguing that symbols are not always necessary since "the world is its own best model. It is always exactly up to date. It always has every detail there is to be known. The trick is to sense it appropriately and often enough."[132] In the 80s and 90s, many cognitive scientists also rejected the symbol processing model of the mind and argued that the body was essential for reasoning, a theory called the embodied mind thesis.[133]

The field of AI, now more than a half a century old, finally achieved some of its oldest goals. It began to be used successfully throughout the technology industry, although somewhat behind the scenes. Some of the success was due to increasing computer power and some was achieved by focusing on specific isolated problems and pursuing them with the highest standards of scientific accountability. Still, the reputation of AI, in the business world at least, was less than pristine. Inside the field there was little agreement on the reasons for AI's failure to fulfill the dream of human level intelligence that had captured the imagination of the world in the 1960s. Together, all these factors helped to fragment AI into competing subfields focused on particular problems or approaches, sometimes even under new names that disguised the tarnished pedigree of "artificial intelligence".[134] AI was both more cautious and more successful than it had ever been.

On 11 May 1997, Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov.[135] The super computer was a specialized version of a framework produced by IBM, and was capable of processing twice as many moves per second as it had during the first match (which Deep Blue had lost), reportedly 200,000,000 moves per second. The event was broadcast live over the internet and received over 74 million hits.[136]

In 2005, a Stanford robot won the DARPA Grand Challenge by driving autonomously for 131 miles along an unrehearsed desert trail.[137] Two years later, a team from CMU won the DARPA Urban Challenge by autonomously navigating 55 miles in an Urban environment while adhering to traffic hazards and all traffic laws.[138] In February 2011, in a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin.[139]

These successes were not due to some revolutionary new paradigm, but mostly on the tedious application of engineering skill and on the tremendous power of computers today.[140] In fact, Deep Blue's computer was 10 million times faster than the Ferranti Mark 1 that Christopher Strachey taught to play chess in 1951.[141] This dramatic increase is measured by Moore's law, which predicts that the speed and memory capacity of computers doubles every two years. The fundamental problem of "raw computer power" was slowly being overcome.

A new paradigm called "intelligent agents" became widely accepted during the 90s.[142] Although earlier researchers had proposed modular "divide and conquer" approaches to AI,[143] the intelligent agent did not reach its modern form until Judea Pearl, Allen Newell and others brought concepts from decision theory and economics into the study of AI.[144] When the economist's definition of a rational agent was married to computer science's definition of an object or module, the intelligent agent paradigm was complete.

An intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. By this definition, simple programs that solve specific problems are "intelligent agents", as are human beings and organizations of human beings, such as firms. The intelligent agent paradigm defines AI research as "the study of intelligent agents". This is a generalization of some earlier definitions of AI: it goes beyond studying human intelligence; it studies all kinds of intelligence.[145]

The paradigm gave researchers license to study isolated problems and find solutions that were both verifiable and useful. It provided a common language to describe problems and share their solutions with each other, and with other fields that also used concepts of abstract agents, like economics and control theory. It was hoped that a complete agent architecture (like Newell's SOAR) would one day allow researchers to build more versatile and intelligent systems out of interacting intelligent agents.[144][146]

AI researchers began to develop and use sophisticated mathematical tools more than they ever had in the past.[147] There was a widespread realization that many of the problems that AI needed to solve were already being worked on by researchers in fields like mathematics, economics or operations research. The shared mathematical language allowed both a higher level of collaboration with more established and successful fields and the achievement of results which were measurable and provable; AI had become a more rigorous "scientific" discipline. Russell & Norvig (2003) describe this as nothing less than a "revolution" and "the victory of the neats".[148][149]

Judea Pearl's highly influential 1988 book[150] brought probability and decision theory into AI. Among the many new tools in use were Bayesian networks, hidden Markov models, information theory, stochastic modeling and classical optimization. Precise mathematical descriptions were also developed for "computational intelligence" paradigms like neural networks and evolutionary algorithms.[148]

Algorithms originally developed by AI researchers began to appear as parts of larger systems. AI had solved a lot of very difficult problems[151] and their solutions proved to be useful throughout the technology industry,[152] such as data mining, industrial robotics, logistics,[153]speech recognition,[154] banking software,[155] medical diagnosis[155] and Google's search engine.[156]

The field of AI receives little or no credit for these successes. Many of AI's greatest innovations have been reduced to the status of just another item in the tool chest of computer science.[157]Nick Bostrom explains "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."[158]

Many researchers in AI in 1990s deliberately called their work by other names, such as informatics, knowledge-based systems, cognitive systems or computational intelligence. In part, this may be because they considered their field to be fundamentally different from AI, but also the new names help to procure funding. In the commercial world at least, the failed promises of the AI Winter continue to haunt AI research, as the New York Times reported in 2005: "Computer scientists and software engineers avoided the term artificial intelligence for fear of being viewed as wild-eyed dreamers."[159][160][161]

In 1968, Arthur C. Clarke and Stanley Kubrick had imagined that by the year 2001, a machine would exist with an intelligence that matched or exceeded the capability of human beings. The character they created, HAL 9000, was based on a belief shared by many leading AI researchers that such a machine would exist by the year 2001.[162]

Marvin Minsky asks "So the question is why didn't we get HAL in 2001?"[163] Minsky believes that the answer is that the central problems, like commonsense reasoning, were being neglected, while most researchers pursued things like commercial applications of neural nets or genetic algorithms. John McCarthy, on the other hand, still blames the qualification problem.[164] For Ray Kurzweil, the issue is computer power and, using Moore's Law, he predicts that machines with human-level intelligence will appear by 2029.[165]Jeff Hawkins argues that neural net research ignores the essential properties of the human cortex, preferring simple models that have been successful at solving simple problems.[166] There are many other explanations and for each there is a corresponding research program underway.

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Ai Ogawa (October 21, 1947 March 20, 2010),[1][2][3][4] born as Florence Anthony, was an American poet and educator. She won the 1999 National Book Award for Poetry for Vice: New and Selected Poems.[5] Ai is known for her mastery of the dramatic monologue as a poetic form, as well as for taking on dark, controversial topics in her work. [1]

Ai, who described herself as half 1/2 Japanese, 1/8 Choctaw-Chickasaw,1/4 Black,1/16 Irish, and Southern Cheyenne, and Comanche, was born in Albany, Texas[1][2][3][4][6][7] in 1947, and she grew up in Tucson, Arizona. She was also raised in Los Angeles, Las Vegas, and San Francisco, with her mother and second stepfather, Sutton Hayes. In 1959, a couple years after the her mother's divorce from Hayes, they moved back to Tucson, Arizona where she completed high school and attended college at the University of Arizona, where she majored in English and Oriental Studies with a concentration in Japanese and a minor in Creative Writing, which she would fully commit to toward the end of her degree.[8] Before starting college, one night during dinner with her mother and third stepfather, Ai learned her biological father was Japanese. Known as Florence Hayes throughout her childhood and undergrad years, it was not until graduate school, when Ai was going to switch her last name back to Anthony that her mother finally told her more details about her past, learning that she had an affair with a Japanese man, Michael Ogawa, after meeting him at a streetcar stop. Learning of the affair had led Ai's first stepfather, whose last name was "Anthony," to beat her mother until family intervened and she was taken to Texas, where her stepfather eventually followed after Ai's birth. Because her mother was still legally married to Anthony at the time, his last name was put on Ai's birth certificate.[9]

The poverty Ai experienced during her childhood affected her and her writing.[10] Ai credits her first writing experience to an assignment in her Catholic school English class to write a letter from the perspective of martyr. Two years after that experience, she began actively writing at the age of 14.[8] History had been one of her many interests since high school.[9]

From 1969 to 1971, Ai attended the University of California at Irvine's M.F.A program where she worked under the likes of Charles Wright and Donald Justice.[8][9] She is the author of "No Surrender," (2010), which was posthumously published after her death, Dread (W. W. Norton & Co., 2003); Vice (1999), which won the National Book Award;[5]Greed (1993); Fate (1991); Sin (1986), which won an American Book Award from the Before Columbus Foundation; Killing Floor (1979), which was the 1978 Lamont Poetry Selection of the Academy of American Poets; and Cruelty (1973).

She also received awards from the Guggenheim Foundation, the National Endowment for the Arts, the Bunting Fellowship Program at Radcliffe College and from various universities. She was a visiting instructor at Binghamton University, State University of New York for the 1973-74 academic year. After winning the National Book Award for "Vice" she became a tenured professor and the vice president of the Native American Faculty and Staff Association at Oklahoma State University and lived in Stillwater, Oklahoma until her death.[11][12]

Ai had considered herself as "simply a writer" rather than a spokesperson for any particular group.[13]

In 1973, she legally changed her last name to Ogawa and her middle name to "Ai" (), translates to "love" in Japanese, which she had been using as a pen name since 1969.[9]

Ai was checked into the hospital on March 17, 2010 for pneumonia. Three days later, Ai died on March 20, 2010 at age 62, in Stillwater, Oklahoma[14] from what turned out to be complications of an advanced, and previously undiagnosed, breast cancer.[15][16]

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The ai file extension is associated with Adobe Illustrator the well known vector graphics editor for the Macintosh and Windows platforms.

AI file format is a widely used format for the exchange of 2D objects. Basic files in this format are simple to write, but files created by applications implementing the full AI specification can be quite large and complex and may be too slow to render.

Simple *.ai files are easy to construct, and a program can create files that can be read by any AI reader or can be printed on any PostScript printer software. Reading AI files is another matter entirely. Certain operations may be very difficult for a rendering application to implement or simulate. In light of this, developers often choose not to render the image from the PostScript-subset line data in the file. However almost all of the image can usually be reconstructed using simple operations.implementation of the PostScript language.

The *.ai files consist of a series of ASCII lines, which may be comments, data, commands, or combinations of commands and data. This data is based on the PDF language specification and older versions of Adobe Illustrator used format which is variant of Adobe Encapsulated PostScirpt (EPS) format.

If The EPS is a slightly limited subset of full PostScript, then Adobe Illustrator AI format is a strictly limited, highly simplified subset of EPS. While EPS can contain virtually any PS command that's not on the verboten list and can include elaborate program flow logic that determines what gets printed when, an AI file is limited to a much smaller number of drawing commands and it contains no programming logic at all. For all practical purposes, each unit of "code" in an AI file represents a drawing object. The program importing the AI reads each object in sequence, start to finish, no detours, no logical side-trips.

MIME: application/postscript

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AI file extension - Open, view and convert .ai files