Artificial intelligence
{{redirect|AI}}

[[Image:P11 kasparov breakout.jpg|thumb|right|280px|[[Garry Kasparov]] playing against [[IBM Deep Blue|Deep Blue]], the first machine to win a chess match against a reigning world champion.]]

'''Artificial intelligence (AI)''' is both the [[intelligence]] of machines and the branch of [[computer science]] which aims to create it.

Major AI textbooks define artificial intelligence as "the study and design of [[intelligent agents]],"<ref>{{Harvnb|Poole|Mackworth|Goebel|1998|loc=[http://www.cs.ubc.ca/spider/poole/ci/ch1.pdf p. 1]}} (who use the term "computational intelligence" as a synonym for artificial intelligence). Other textbooks that define AI this way include {{Harvtxt|Nilsson|1998}}, and {{Harvtxt|Russell|Norvig|2003}} (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field" {{Harv|Russell|Norvig|2003|p=55}}</ref>
where an [[intelligent agent]] is a system that perceives its environment and takes actions which maximize its chances of success.<ref>This definition, in terms of goals, actions, perception and environment, is due to {{Harvtxt|Russell|Norvig|2003}}. Other definitions also include knowledge and learning as additional criteria. See also [http://erg4146.casaccia.enea.it/wwwerg26701/gad-zyt.htm ''Abstract Intelligent Agents: Paradigms, Foundations and Conceptualization Problems''], A.M. Gadomski, J.M. Zytkow, in "Abstract Intelligent Agent, 2". Printed by ENEA, Rome 1995, ISSN/1120-558X]</ref>
[[John McCarthy (computer scientist)|John McCarthy]], who coined the term in 1956,<ref>Although there is some controversy on this point (see {{Harvnb|Crevier|1993|p=50}}), [[John McCarthy|McCarthy]] states unequivocally "I came up with the term" in a c|net interview. (See [http://news.com.com/Getting+machines+to+think+like+us/2008-11394_3-6090207.html Getting Machines to Think Like Us].)</ref>
defines it as "the science and engineering of making intelligent machines."<ref>See [[John McCarthy (computer scientist)|John McCarthy]], [http://www-formal.stanford.edu/jmc/whatisai/whatisai.html What is Artificial Intelligence?]</ref>

Among the traits that researchers hope machines will exhibit are [[:#Deduction, reasoning, problem solving|reasoning]], [[#Knowledge representation|knowledge]], [[#Planning|planning]], [[#Learning|learning]], [[#Natural language processing|communication]], [[#Perception|perception]] and the ability to [[#Motion and manipulation|move]] and manipulate objects.<ref name="I">
This list of intelligent traits is based on the topics covered by the major AI textbooks, including: {{Harvnb|Russell|Norvig|2003}}, {{Harvnb|Luger|Stubblefield|2004}}, {{Harvnb|Poole|Mackworth|Goebel|1998}} and {{Harvnb|Nilsson|1998}}.</ref>
[[#General intelligence|General intelligence]] (or "[[strong AI]]") has not yet been achieved and is a long-term goal of some AI research.<ref name="GI">
General intelligence ([[strong AI]]) is discussed by popular introductions to AI, such as: {{Harvnb|Kurzweil|1999}}, {{Harvnb|Kurzweil|2005}}, {{Harvnb|Hawkins|Blakeslee|2004}}</ref>

AI research uses tools and insights from many fields, including [[computer science]], [[psychology]], [[philosophy]], [[neuroscience]], [[cognitive science]], [[computational linguistics|linguistics]], [[ontology (information science)|ontology]], [[operations research]], [[computational economics|economics]], [[control theory]], [[probability]], [[optimization (mathematics)|optimization]] and [[logic]].<ref>{{Harvnb|Russell|Norvig|2003|pp=5-16}}</ref>
AI research also overlaps with tasks such as [[robotics]], [[control system]]s, [[automated planning and scheduling|scheduling]], [[data mining]], [[logistics]], [[speech recognition]], [[facial recognition system|facial recognition]] and many others.<ref>See [http://www.aaai.org/AITopics/html/applications.html AI Topics: applications]</ref>

Other names for the field have been proposed, such as [[computational intelligence]],<ref name="PMGp1">{{Harvnb|Poole|Mackworth|Goebel|1998|loc=[http://www.cs.ubc.ca/spider/poole/ci/ch1.pdf p. 1]}}</ref>
[[synthetic intelligence]],<ref name="PMGp1" />
[[intelligent systems]],<ref>The name of the journal [http://www.computer.org/portal/site/intelligent Intelligent Systems]</ref>
or computational rationality.<ref>{{Harvnb|Russell|Norvig|2003|p=17}}</ref>

{{portal}}

== Perspectives on AI ==
=== AI in myth, fiction and speculation ===
{{Main|artificial intelligence in fiction|ethics of artificial intelligence|transhumanism|Technological singularity}}

<!-- AN ANCIENT DREAM (MCCORDUCK'S THESIS) -->
Humanity has imagined in great detail the implications of thinking machines or artificial beings. They appear in [[Greek myth]]s, such as [[Talos]] of [[Crete]], the golden robots of [[Hephaestus]] and [[Pygmalion (mythology)|Pygmalion's]] [[Galatea (mythology)|Galatea]].<ref>{{Harvnb|McCorduck|2004|p=5}}, {{Harvnb|Russell|Norvig|2003|p=939}}</ref> The earliest known humanoid robots (or  [[automaton]]s) were [[cult image|sacred statue]]s worshipped in [[Egypt]] and [[Greece]], believed to have been endowed with genuine consciousness by craftsman.<ref>The Egyptian statue of [[Amun]] is discussed by {{Harvtxt|Crevier|1993|p=1}}. {{Harvtxt|McCorduck|2004|pp=6-9}} discusses Greek statues.  [[Hermes Trismegistus]] expressed the common belief that with these statues, craftsman had reproduced "the true nature of the gods", their ''sensus'' and ''spiritus''. McCorduck makes the connection between sacred automatons and [[Mosaic law]] (developed around the same time), which expressly forbids the worship of robots.</ref> In the sixteenth century, the [[alchemist]] [[Paracelsus]] claimed to have created artificial beings.<ref>{{Harvnb|McCorduck|2004|p=13-14}} (Paracelsus)</ref> Realistic clockwork imitations of human beings have been built by people such as [[King Mu of Zhou#Robotics|Yan Shi]],<ref>{{Harvnb|Needham|1986|p=53}}</ref> [[Hero of Alexandria]],<ref>{{Harvnb|McCorduck|2004|p=6}}</ref> [[Al-Jazari]]<ref>[http://www.shef.ac.uk/marcoms/eview/articles58/robot.html A Thirteenth Century Programmable Robot]</ref> and [[Wolfgang von Kempelen]].<ref>{{Harvnb|McCorduck|2004|p=17}}</ref>

<!--ROBOT RIGHTS / THE ABILITY TO SUFFER -->
In modern fiction, beginning with [[Mary Shelley]]'s classic ''[[Frankenstein]],'' writers have explored the [[ethics of artificial intelligence|ethical]] issues presented by thinking machines.<ref>{{Harvtxt|McCorduck|2004|p=190-25}} discusses ''[[Frankenstein]]'' and identifies the key ethical issues as scientific hubris and the suffering of the monster, e.g. [[robot rights]].</ref> If a machine can be created that has intelligence, can it also ''feel''?  If it can feel, does it have the same rights as a human being? This is a key issue in ''[[Frankenstein]]'' as well as in modern science fiction: for example, the film ''[[Artificial Intelligence: A.I.]]'' considers a machine in the form of a small boy which has been given the ability to feel human emotions, including, tragically, the capacity to suffer. This issue is also being considered by [[futurist]]s, such as California's [[Institute for the Future]] under the name "[[robot rights]]",<ref>[http://news.bbc.co.uk/2/hi/technology/6200005.stm Robots could demand legal rights]</ref> although many critics believe that the discussion is premature.<ref>See the Times Online, [http://www.timesonline.co.uk/tol/news/uk/science/article1695546.ece Human rights for robots? We’re getting carried away]</ref><ref>[[robot rights]]: {{Harvnb|Russell|Norvig|p=964}}</ref>

<!-- IMPACT ON SOCIETY -->
[[Science fiction]] writers and [[futurist]]s have also speculated on the technology's potential impact on humanity. In fiction, AI has appeared as a servant ([[R2D2]] in ''[[Star Wars]]''), a comrade ([[Data (Star Trek)|Lt. Commander Data]] in ''[[Star Trek]]''), an extension to human abilities (''[[Ghost in the Shell]]''), a conqueror (''[[The Matrix]]''), a dictator (''[[With Folded Hands]]'') and an exterminator (''[[Terminator (series)|Terminator]]'', ''[[Battlestar Galactica (re-imagining)|Battlestar Galactica]]''). Some realistic potential consequences of AI are decreased human labor demand,<ref> {{Harvtxt|Russell|Norvig|2003|p=960-961}}</ref> the enhancement of human ability or experience,<ref>{{Harvnb|Kurzweil|2004}}</ref> and a need for redefinition of human identity and basic values.<ref>[[Joseph Weizenbaum]] (the AI researcher who developed the first [[chatterbot]] program, [[ELIZA]]) argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life. Weizenbaum: {{Harvnb|Crevier|1993|pp=132−144}}, {{Harvnb|McCorduck|2004|pp=356-373}}, {{Harvnb|Russell|Norvig|2003|p=961}} and {{Harvnb|Weizenbaum|1976}}</ref>

<!-- PROJECTIONS / SINGULARITY -->
[[Futurist]]s estimate the capabilities of machines using [[Moore's Law]], which measures the relentless exponential improvement in digital technology with uncanny accuracy. [[Ray Kurzweil]] has calculated that [[desktop computer]]s will have the same processing power as human brains by the year 2029, and that by 2045 artificial intelligence will reach a point where it is able to improve ''itself'' at a rate that far exceeds anything conceivable in the past, a scenario that [[science fiction]] writer [[Vernor Vinge]] named the "[[technological singularity]]".<ref name=TS>[[Singularity]], [[transhumanism]]: {{Harvnb|Kurzweil|2005}}, {{Harvnb|Russell|Norvig|2003|p=963}}</ref>

<!-- EVOLUTION / TRANSHUMANISM  -->
"Artificial intelligence is the next stage in evolution," [[Edward Fredkin]] said in the 1980s,<ref>Quoted in {{Harvtxt|McCorduck|2004|p=401}}</ref> expressing an idea first proposed by [[Samuel Butler (novelist)|Samuel Butler]]'s ''[[Darwin Among the Machines]]'' (1863), and expanded upon by [[George Dyson (science historian)|George Dyson]] in his book of the same name (1998). Several [[futurist]]s and [[science fiction]] writers have predicted that human beings and machines will merge in the future into [[cyborg]]s that are more capable and powerful than either. This idea, called [[transhumanism]], has roots in [[Aldous Huxley]] and [[Robert Ettinger]], is now associated with [[robotics|robot]] designer [[Hans Moravec]], [[cybernetics|cyberneticist]] [[Kevin Warwick]] and [[Ray Kurzweil]].<ref name=TS/> [[Transhumanism]] has been illustrated in fiction as well, for example on the [[manga]] ''[[Ghost in the Shell]]''

=== History of AI research ===
<!-- THIS IS A SOCIAL HISTORY OF AI RESEARCH. TECHNICAL HISTORY IS COVERED IN THE "APPROACHES" AND "TOOLS" SECTIONS. -->
{{Main|history of artificial intelligence|timeline of artificial intelligence}}

In the middle of the 20th century, a handful of scientists began a new approach to building intelligent machines, based on recent discoveries in [[neurology]], a new mathematical theory of [[information]], an understanding of control and stability called [[cybernetic]]s, and above all, by the invention of the [[digital computer]], a machine based on the abstract essence of mathematical reasoning.<ref name="CYBER">Among the researchers who laid the foundations of the [[theory of computation]], [[cybernetic]]s, [[information theory]] and [[neural networks]] were [[Claude Shannon]], [[Norbert Weiner]], [[Warren McCullough]], [[Walter Pitts]], [[Donald Hebb]], [[Donald McKay (computer scientist)|Donald McKay]], [[Alan Turing]] and [[John Von Neumann]]. {{Harvnb|McCorduck|2004|pp=51-107}}, {{Harvnb|Crevier|1993|pp=27-32}}, {{Harvnb|Russell|Norvig|2003|pp=15,940}}, {{Harvnb|Moravec|1988|p=3}}. </ref>

The field of modern AI research was founded at conference on the campus of [[Dartmouth College]] in the summer of 1956.<ref>{{Harvnb|Crevier|1993|pp=47-49}}, {{Harvnb|Russell|Norvig|2003|p=17}}</ref>
Those who attended would become the leaders of AI research for many decades, especially [[John McCarthy (computer scientist)|John McCarthy]], [[Marvin Minsky]], [[Allen Newell]] and [[Herbert Simon]], who founded AI laboratories at [[MIT]], [[Carnegie Mellon University|CMU]] and [[Stanford]]. They and their students wrote programs that were, to most people, simply astonishing:<ref>Russell and Norvig write "it was astonishing whenever a computer did anything kind of smartish." {{Harvnb|Russell|Norvig|2003|p=18}}</ref>
computers were solving word problems in algebra, proving logical theorems and speaking English.<ref>{{Harvnb|Crevier|1993|pp=52-107}}, {{Harvnb|Moravec|1988|p=9}} and {{Harvnb|Russell|Norvig|2003|p=18-21}}. The programs described are [[Daniel Bobrow]]'s [[STUDENT (computer program)|STUDENT]], [[Allen Newell|Newell]] and [[Herbert Simon|Simon]]'s [[Logic Theorist]] and [[Terry Winograd]]'s [[SHRDLU]].</ref>
By the middle 60s their research was heavily funded by the [[DARPA|U.S. Department of Defense]]<ref>{{Harvnb|Crevier|1993|pp=64-65}}</ref>
and they were optimistic about the future of the new field:
* 1965, [[H. A. Simon]]: "[M]achines will be capable, within twenty years, of doing any work a man can do"<ref>{{Harvnb|Simon|1965|p=96}} quoted in {{Harvnb|Crevier|1993|p=109}}</ref>
* 1967, [[Marvin Minsky]]: "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."<ref>{{Harvnb|Minsky|1967|p=2}} quoted in {{Harvnb|Crevier|1993|p=109}}</ref>

These predictions, and many like them, would not come true. They had failed to recognize the difficulty of some of the problems they faced.<ref>See [[History of artificial intelligence#The problems|History of artificial intelligence — the problems]].</ref>
In 1974, in response to the criticism of England's [[Sir James Lighthill]] and ongoing pressure from Congress to fund more productive projects, the U.S. and British governments cut off all undirected, exploratory research in AI. This was the first [[AI Winter]].<ref>{{Harvnb|Crevier|1993|pp=115-117}}, {{Harvnb|Russell|Norvig|2003|p=22}}, {{Harvnb|NRC|1999}} under "Shift to Applied Research Increases Investment." and also see Howe, J. [http://www.dai.ed.ac.uk/AI_at_Edinburgh_perspective.html ''"Artificial Intelligence at Edinburgh University: a Perspective"'']</ref>

In the early 80s, AI research was revived by the commercial success of [[expert systems]] (a form of AI program that simulated the knowledge and analytical skills of one or more human experts) and by 1985 the market for AI had reached more than a billion dollars.<ref>{{Harvnb|Crevier|1993|pp=161-162,197-203}} and {{Harvnb|Russell|Norvig|2003|p=24}}</ref>
[[Marvin Minsky|Minsky]] and others warned the community that enthusiasm for AI had spiraled out of control and that disappointment was sure to follow.<ref>{{Harvnb|Crevier|1993|p=203}}</ref>
Beginning with the collapse of the [[Lisp Machine]] market in 1987, AI once again fell into disrepute, and a second, more lasting [[AI Winter]] began.<ref>{{Harvnb|Crevier|1993|pp=209-210}}</ref>

In the 90s and early 21st century AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence was adopted throughout the technology industry, providing the heavy lifting for [[logistics]], [[data mining]], [[medical diagnosis]] and many other areas.<ref>{{Harvnb|Russell|Norvig|p=28}},{{Harvnb|NRC|1999}} under "Artificial Intelligence in the 90s"</ref>
The success was due to several factors: the incredible power of computers today (see [[Moore's law]]), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.<ref>{{Harvnb|Russell|Norvig|pp=25-26}}</ref>

=== Philosophy of AI ===
{{portalpar|Mind and Brain}}

{{main|philosophy of artificial intelligence}}

[[Image:Brain 090407.jpg|thumb|238px|right|Can the brain be simulated by a digital computer? If it can, then would the simulation have a [[mind]] in the same sense that people do?]]
In a [[Computing Machinery and Intelligence|classic 1950 paper]], [[Alan Turing]] posed the question "Can Machines Think?" In the years since, the [[philosophy of artificial intelligence]] has attempted to answer it.<ref>All of these positions are mentioned in standard discussions of the subject, such as {{Harvnb|Russell|Norvig|2003|pp=947-960}} and {{Harvnb|Fearn|2007|pp=38-55}}</ref>
* [[Turing Test|Turing's "polite convention"]]: ''If a machine acts as intelligently as a human being, then it is as intelligent as a human being.'' [[Alan Turing]] theorized that, ultimately, we can only judge the intelligence of machine based on its behavior. This theory forms the basis of the [[Turing test]].<ref>{{Harvnb|Turing|1950}}, {{Harvnb|Haugeland|1985|pp=6-9}}, {{Harvnb|Crevier|1993|p=24}}, {{Harvnb|Russell|Norvig|2003|pp=2-3 and 948}}</ref>
* The [[Dartmouth Conferences|Dartmouth proposal]]: ''Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it.'' This assertion was printed in the proposal for the [[Dartmouth Conferences|Dartmouth Conference]] of 1956, and represents the position of most working AI researchers.<ref>{{Harvnb|McCarthy|Minsky|Rochester|Shannon|1955}} See also {{Harvnb|Crevier|1993|p=28}}</ref>
* [[Alan Newell|Newell]] and [[Herbert Simon|Simon]]'s physical symbol system hypothesis: ''A [[physical symbol system]] has the necessary and sufficient means of general intelligent action.'' This statement claims that the essence of intelligence is symbol manipulation.<ref>{{Harvnb|Newell|Simon|1963}} and {{Harvnb|Russell|Norvig|2003|p=18}}</ref> [[Hubert Dreyfus]] argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a "feel" for the situation rather than explicit symbolic knowledge.<ref>Dreyfus criticized a version of the [[physical symbol system]] hypothesis that he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules". {{Harvnb|Dreyfus|1992|p=156}}. See also {{Harvnb|Dreyfus|Dreyfus|1986}}, {{Harvnb|Russell|Norvig|2003|pp=950-952}}, {{Harvnb|Crevier|1993|120-132}} and {{Harvnb|Hearn|2007|pp=50-51}}</ref>
* [[Gödel's incompleteness theorem]]: ''A [[physical symbol system]] can not prove all true statements.'' [[Roger Penrose]] is among those who claim that Gödel's theorem limits what machines can do.<ref> This is a paraphrase of the most important implication of Gödel's theorems, according {{Harvtxt|Hofstadter|1979}}. See also {{Harvnb|Russell|Norvig|2003|p=949}}, {{Harvnb| Gödel|1931}}, {{Harvnb|Church|1936}}, {{Harvnb|Kleene|1935}}, {{Harvnb|Turing|1937}}, {{Harvnb|Turing|1950}} under “(2) The Mathematical Objection”
</ref>
* [[John Searle|Searle]]'s "strong AI position": ''A [[physical symbol system]] can have a [[mind]] and [[consciousness|mental states]].'' Searle counters this assertion with his [[Chinese room]] argument, which asks us to look ''inside'' the computer and try to find where the "mind" might be.<ref name="SWAI">{{Harvnb|Searle|1980}}. See also {{Harvtxt|Russell|Norvig|2003|p=947}}: "The assertion that machines could possibly act intelligently (or, perhaps better, act as if they were intelligent) is called the 'weak AI' hypothesis by philosophers, and the assertion that machines that do so are actually thinking (as opposed to simulating thinking) is called the 'strong AI' hypothesis," although Searle's arguments, such as the [[Chinese Room]], apply only to [[physical symbol system]]s, not to machines in general (he would consider the brain a machine). Also, notice that the positions as Searle states them don't make any commitment to how ''much'' intelligence the system has: it is one thing to say a machine can act intelligently, it is another to say it can act as intelligently as a human being.</ref>
* The [[artificial brain]] argument: ''The brain can be simulated.'' [[Hans Moravec]], [[Ray Kurzweil]] and others have argued that it is technologically feasible to copy the brain directly into hardware and software, and that such a simulation will be essentially identical to the original. This argument combines the idea that a [[Turing complete|suitably powerful]] machine can simulate any process, with the [[materialist]] idea that the [[mind]] is the result of a physical process in the [[brain]].<ref>{{Harvnb|Moravec|1988}} and {{Harvnb|Kurzweil|2005|p=262}}. Also see {{Harvnb|Russell|Norvig|p=957}} and {{Harvnb|Crevier|1993|pp=271 and 279}}. The most extreme form of this argument (the brain replacement scenario) was put forward by [[Clark Glymour]] in the mid-70s and was touched on by [[Zenon Pylyshyn]] and [[John Searle]] in 1980.
</ref>

== AI research ==
=== Problems of AI ===

While there is no universally accepted definition of intelligence,<ref>"We cannot yet characterize in general what kinds of computational procedures we want to call intelligent." [[John McCarthy (computer scientist)|John McCarthy]], [http://www-formal.stanford.edu/jmc/whatisai/node1.html Basic Questions]</ref>
AI researchers have studied several traits that are considered essential.<ref name="I" />

====Deduction, reasoning, problem solving ====<!-- This is linked to in the introduction -->
Early AI researchers developed algorithms that imitated the process of conscious, step-by-step reasoning that human beings use when they solve puzzles, play board games, or make logical deductions.<ref>Problem solving, puzzle solving, game playing and deduction: {{Harvnb|Russell|Norvig|2003|loc=chpt. 3-9}}, {{Harvnb|Poole|Mackworth|Goebel|1998|chpt. 2,3,7,9}}, {{Harvnb|Luger|Stubblefield|2004|loc=chpt. 3,4,6,8}}, {{Harvnb|Nilsson|loc=chpt. 7-12}}.</ref>
By the late 80s and 90s, AI research had also developed highly successful methods for dealing with [[uncertainty|uncertain]] or incomplete information, employing concepts from [[probability]] and [[economics]].<ref>Uncertain reasoning: {{Harvnb|Russell|Norvig|2003|pp=452-644}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=345-395}}, {{Harvnb|Luger|Stubblefield|2004|pp=333-381}}, {{Harvnb|Nilsson|1998|loc=chpt. 19}}</ref>

For difficult problems, most of these algorithms can require enormous computational resources — most experience a "[[combinatorial explosion]]": the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. The search for more efficient problem solving algorithms is a high priority for AI research.<ref>[[Intractable|Intractability and efficiency]] and the [[combinatorial explosion]]: {{Harvnb|Russell|Norvig|2003|pp=9, 21-22}}</ref>

It is not clear, however, that conscious human reasoning is any more efficient when faced with a difficult abstract problem. [[Cognitive science|Cognitive scientists]] have demonstrated that human beings solve most of their problems using [[unconscious]] reasoning, rather than the conscious, step-by-step deduction that early AI research was able to model.<ref>
Several famous examples: {{Harvtxt|Wason|1966}} showed that people do poorly on completely abstract problems, but if the problem is restated to allowed the use of intuitive [[social intelligence]], performance dramatically improves. (See [[Wason selection task]]) {{Harvtxt|Tversky|Slovic|Kahnemann|1982}} have shown that people are terrible at elementary problems that involve uncertain reasoning. (See [[list of cognitive biases]] for several examples). {{Harvtxt|Lakoff|Nunez|2000}} have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See [[Where Mathematics Comes From]])
</ref>
[[Embodied cognitive science]] argues that unconscious [[sensorimotor]] skills are essential to our problem solving abilities. It is hoped that sub-symbolic methods, like [[computational intelligence]] and [[situated]] AI, will be able to model these instinctive skills. The problem of unconscious problem solving, which forms part of our [[commonsense reasoning]], is largely unsolved{{Dubious|date=April 2008}}.

====Knowledge representation====<!-- This is linked to in the introduction -->
{{Main|knowledge representation|commonsense knowledge}}

[[Knowledge representation]]<ref>[[Knowledge representation]]: {{Harvnb|ACM|1998|loc=I.2.4}}, {{Harvnb|Russell|Norvig|2003|pp=320-363}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=23-46, 69-81, 169-196, 235-277, 281-298, 319-345}}, {{Harvnb|Luger|Stubblefield|2004|pp=227-243}}, {{Harvnb|Nilsson|1998|loc=chpt. 18}}</ref>
and [[knowledge engineering]]<ref>[[Knowledge engineering]]: {{Harvnb|Russell|Norvig|2003|pp=260-266}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=199-233}}, {{Harvnb|Nilsson|1998|loc=chpt. ~17.1-17.4}}</ref>
are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects;<ref name="DL">Representing categories and relations: [[Semantic network]]s, [[description logic]]s, [[inheritance (computer science)|inheritance]] (including [[Frame (artificial intelligence)|frame]]s and [[scripts (artificial intelligence)|scripts]]): {{Harvnb|Russell|Norvig|2003|pp=349-354}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=174-177}}, {{Harvnb|Luger|Stubblefield|2004|pp=248-258}}, {{Harvnb|Nilsson|1998|loc=chpt. 18.3}}</ref>
situations, events, states and time;<ref name="SC">Representing events and time: [[Situation calculus]], [[event calculus]], [[fluent calculus]] (including solving the [[frame problem]]): {{Harvnb|Russell|Norvig|2003|pp=328-341}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=281-298}}, {{Harvnb|Nilsson|1998|loc=chpt. 18.2}}</ref>
causes and effects;<ref name="CC">[[Causality#causal calculus|Causal calculus]]: {{Harvnb|Poole|Mackworth|Goebel|1998|pp=335-337}}</ref>
knowledge about knowledge (what we know about what other people know);<ref name="BC">Representing knowledge about knowledge: [[Belief calculus]], [[modal logic]]s: {{Harvnb|Russell|Norvig|2003|pp=341-344}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=275-277}}</ref>
and many other, less well researched domains. A complete representation of "what exists" is an [[ontology (computer science)|ontology]]<ref>[[Ontology (computer science)|Ontology]]: {{Harvnb|Russell|Norvig|2003|pp=320-328}}</ref>
(borrowing a word from traditional [[philosophy]]), of which the most general are called [[upper ontology|upper ontologies]].

Among the most difficult problems in knowledge representation are:

* ''Default reasoning and the [[qualification problem]]'': Many of the things people know take the form of "working assumptions." For example, if a bird comes up in conversation, people typically picture an animal that is fist sized, sings, and flies. None of these things are true about birds in general. [[John McCarthy (computer scientist)|John McCarthy]] identified this problem in 1969<ref>{{Harvnb|McCarthy|Hayes|1969}}</ref> as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.<ref name="NML">Default reasoning and [[default logic]], [[non-monotonic logic]]s, [[circumscription]], [[closed world assumption]], [[abduction]] (Poole ''et al.'' places abduction under "default reasoning". Luger ''et al.'' places this under "uncertain reasoning"): {{Harvnb|Russell|Norvig|2003|pp=354-360}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=248-256, 323-335}}, {{Harvnb|Luger|Stubblefield|2004|pp=335-363}}, {{Harvnb|Nilsson|1998|loc=~18.3.3}}</ref>
* ''Unconscious knowledge'': Much of what people know isn't represented as "facts" or "statements" that they could actually say out loud. They take the form of intuitions or tendencies and are represented in the brain unconsciously and sub-symbolically. This unconscious knowledge informs, supports and provides a context for our conscious knowledge. As with the related problem of unconscious reasoning, it is hoped that [[situated]] AI or [[computational intelligence]] will provide ways to represent this kind of knowledge.
* ''The breadth of [[common sense knowledge]]'': The number of atomic facts that the average person knows is astronomical. Research projects that attempt to build a complete knowledge base of [[commonsense knowledge]], such as [[Cyc]], require enormous amounts of tedious step-by-step ontological engineering — they must be built, by hand, one complicated concept at a time.<ref>{{Harvnb|Crevier|1993|pp=113-114}}, {{Harvnb|Moravec|1988|p=13}}, {{Harvnb|Lenat|1989}} (Introduction), {{Harvnb|Russell|Norvig|2003|p=21}}</ref>

====Planning====<!-- This is linked to in the introduction -->
{{Main|automated planning and scheduling}}

Intelligent agents must be able to set goals and achieve them.<ref>[[automated planning and scheduling|Planning]]: {{Harvnb|ACM|1998|loc=~I.2.8}}, {{Harvnb|Russell|Norvig|2003|pp= 375-459}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=281-316}}, {{Harvnb|Luger|Stubblefield|2004|pp=314-329}}, {{Harvnb|Nilsson|1998|loc=chpt. 10.1-2, 22}}</ref>
They need a way to visualize the future: they must have a representation of the state of the world and be able to make predictions about how their actions will change it. They must also attempt to determine the [[utility]] or "value" of the choices available to it.<ref name = "IVT"/>

In some planning problems, the agent can assume that it is the only thing acting on the world and it can be certain what the consequences of its actions may be.<ref>[[Classical planning]]: {{Harvnb|Russell|Norvig|2003|pp=375-430}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=281-315}}, {{Harvnb|Luger|Stubblefield|2004|pp=314-329}}, {{Harvnb|Nilsson|1998|loc=chpt. 10.1-2, 22}}</ref>
However, if this is not true, it must periodically check if the world matches its predictions and it must change its plan as this becomes necessary, requiring the agent to reason under uncertainty.<ref>Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning: {{Harvnb|Russell|Norvig|2003|pp=430-449}}</ref>

[[Multi-agent planning]] tries to determine the best plan for a community of [[agent]]s, using [[cooperation]] and [[competition]] to achieve a given goal. [[Emergent behavior]] such as this is used by both [[evolutionary algorithm]]s and [[swarm intelligence]].<ref>Multi-agent planning and emergent behavior: {{Harvnb|Russell|Norvig|2003|pp=449-455}}</ref>

====Learning====<!-- This is linked to in the introduction -->
{{Main|machine learning}}

Important [[machine learning]]<ref>[[machine learning|Learning]]: {{Harvnb|ACM|1998|loc=I.2.6}}, {{Harvnb|Russell|Norvig|2003|pp=649-788}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=397-438}}, {{Harvnb|Luger|Stubblefield|2004|pp=385-542}}, {{Harvnb|Nilsson|1998|loc=chpt. 3.3 , 10.3, 17.5, 20}}</ref>
problems are:

* [[Unsupervised learning]]: find a model that matches a stream of input "experiences", and be able to predict what new "experiences" to expect.
* [[Supervised learning]], such as [[statistical classification|classification]] (be able to determine what category something belongs in, after seeing a number of examples of things from each category), or [[regression]] (given a set of numerical input/output examples, discover a continuous function that would generate the outputs from the inputs).
* [[Reinforcement learning]]:<ref>[[Reinforcement learning]]: {{Harvnb|Russell|Norvig|2003|pp=763-788}}, {{Harvnb|Luger|Stubblefield|2004|pp=442-449}}
</ref> the agent is rewarded for good responses and punished for bad ones. (These can be analyzed in terms [[decision theory]], using concepts like [[utility (economics)|utility]]).

====Natural language processing====<!-- This is linked to in the introduction -->
{{Main|natural language processing}}

[[Natural language processing]]<ref>[[Natural language processing]]: {{Harvnb|ACM|1998|loc=I.2.7}}, {{Harvnb|Russell|Norvig|2003|pp=790-831}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=91-104}}, {{Harvnb|Luger|Stubblefield|2004|pp=591-632}}</ref>
gives machines the ability to read and understand the languages human beings speak. Many researchers hope that a sufficiently powerful natural language processing system would be able to acquire knowledge on its own, by reading the existing text available over the internet. Some straightforward applications of natural language processing include [[information retrieval]] (or [[text mining]]) and [[machine translation]].<ref>
Applications of natural language processing, including [[information retrieval]] (i.e. [[text mining]]) and [[machine translation]] {{Harvnb|Russell|Norvig|2003|pp=840-857}}, {{Harvnb|Luger|Stubblefield|2004|pp=623-630}}</ref>

====Motion and manipulation====<!-- This is linked to in the introduction -->
[[Image:Honda ASIMO Walking Stairs.JPG|thumb|200px|[[ASIMO]] uses sensors and intelligent algorithms to avoid obstacles and navigate stairs.]]
{{Main|robotics}}

The field of [[robotics]]<ref>[[Robotic]]s: {{Harvnb|ACM|1998|loc=I.2.9}}, {{Harvnb|Russell|Norvig|2003|pp=901-942}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=443-460}}</ref>
is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation<ref name="CS">Moving and [[configuration space]]: {{Harvnb|Russell|Norivg|pp=916-932}}</ref>
and [[motion planning|navigation]], with sub-problems of [[localization]] (knowing where you are), [[robotic mapping|mapping]] (learning what is around you) and [[motion planning]] (figuring out how to get there).<ref>[[Robotic mapping]] (localization, etc) {{Harvnb|Russell|Norvig|pp=908-915}} </ref>

====Perception====<!-- This is linked to in the introduction -->
{{Main|machine perception|computer vision|speech recognition}}

[[Machine perception]]<ref>[[Machine perception]]: {{Harvnb|Russell|Norvig|2003|pp=537-581, 863-898}}, {{Harvnb|Nilsson|1998|loc=~chpt. 6}}</ref>
is the ability to use input from sensors (such as cameras, microphones, sonar and others more exotic) to deduce aspects of the world. [[Computer vision]]<ref>[[Computer vision]]: {{Harvnb|ACM|1998|loc=I.2.10}}, {{Harvnb|Russell|Norvig|2003|pp=863-898}}, {{Harvnb|Nilsson|1998|loc=chpt. 6}}</ref>
is the ability to analyze visual input. A few selected subproblems are [[speech recognition]],<ref>[[Speech recognition]]: {{Harvnb|ACM|1998|loc=~I.2.7}}, {{Harvnb|Russell|Norvig|2003|pp=568-578}}</ref>
[[facial recognition]] and [[object recognition]].<ref>[[Object recognition]]: {{Harvnb|Russell|Norvig|2003|pp=885-892}}</ref>

====Social intelligence====<!-- This is linked to in the introduction -->
{{Main|affective computing}}
[[Image:Wikimania 2006 POLIMEREK 100-0093 IMG.JPG|thumb|200px|[[Kismet (robot)|Kismet]], a robot with rudimentary social skills.]]

Emotion and social skills play two roles for an intelligent agent:<ref>{{Harvnb|Minsky|2007}}, {{Harvnb|Picard|1997}}</ref>
* It must be able to predict the actions of others, by understanding their motives and emotional states. (This involves elements of [[game theory]], [[decision theory]], as well as the ability to model human emotions and the perceptual skills to detect emotions.)
* For good [[human-computer interaction]], an intelligent machine also needs to ''display'' emotions — at the very least it must appear polite and sensitive to the humans it interacts with. At best, it should appear to have normal emotions itself.

====Creativity====<!-- This is linked to in the introduction -->
{{Main|Computational creativity}}

A sub-field of AI addresses [[creativity]] both theoretically (from a philosophical and psychological perspective) and practically (via specific implementations of systems that generate outputs that can be considered creative).

====General intelligence====<!-- This is linked to in the introduction -->
{{Main|strong AI|AI-complete}}

Most researchers hope that their work will eventually be incorporated into a machine with ''general'' intelligence (known as [[strong AI]]), combining all the skills above and exceeding human abilities at most or all of them.<ref name="GI" /> A few believe that [[anthropomorphic]] features like [[artificial consciousness]] or an [[artificial brain]] may be required for such a project.

Many of the problems above are considered [[AI-complete]]: to solve one problem, you must solve them all. For example, even a straightforward, specific task like [[machine translation]] requires that the machine follow the author's argument ([[#Deduction, reasoning, problem solving|reason]]), know what it's talking about ([[#Knowledge representation|knowledge]]), and faithfully reproduce the author's intention ([[#Social intelligence|social intelligence]]). [[Machine translation]], therefore, is believed to be AI-complete: it may require [[strong AI]] to be done as well as humans can do it.<ref>{{Harvnb|Shapiro|1992|p=9}}</ref>

=== Approaches to AI ===

There are as many approaches to AI as there are AI researchers—any coarse categorization is likely to be unfair to someone. Artificial intelligence communities have grown up around particular problems, institutions and researchers, as well as the theoretical insights that define the approaches described below. Artificial intelligence is a young science and is still a fragmented collection of subfields. At present, there is no established unifying theory that links the subfields into a coherent whole.

==== Cybernetics and brain simulation ====

[[Image:ArtificialFictionBrain.png|thumb|right|280px|The [[human brain]] provides inspiration for artificial intelligence researchers, however there is no consensus on how closely it should be [[computer simulation|simulated]].]]
In the 40s and 50s, a number of researchers explored the connection between [[neurology]], [[information theory]], and [[cybernetics]]. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as [[W. Grey Walter]]'s [[Turtle (robot)|turtles]] and the [[Johns Hopkins Beast]]. Many of these researchers gathered for meetings of the [[Teleological Society]] at Princeton and the [[Ratio Club]] in England.<ref name="CYBER"/>

==== Traditional symbolic AI ====

When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: [[Carnegie Mellon University|CMU]], [[Stanford]] and [[MIT]], and each one developed its own style of research. [[John Haugeland]] named these approaches to AI "good old fashioned AI" or "[[GOFAI]]".<ref>

{{Harvnb|Haugeland|1985|pp=112-117}}

</ref>

; Cognitive simulation:[[Economist]] [[Herbert Simon]] and [[Alan Newell]] studied human problem solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as [[cognitive science]], [[operations research]] and [[management science]]. Their research team performed [[psychology|psychological]] experiments to demonstrate the similarities between human problem solving and the programs (such as their "[[General Problem Solver]]") they were developing. This tradition, centered at [[Carnegie Mellon University]],<ref>Then called [[Carnegie Tech]]</ref> would eventually culminate in the development of the [[Soar (cognitive architecture)|Soar]] architecture in the middle 80s.<ref>

{{Harvnb|Crevier|1993|pp=52-54, 258-263}}, {{Harvnb|Nilsson|1998|p=275}}

</ref>

; Logical AI:Unlike [[Alan Newell|Newell]] and [[Herbert Simon|Simon]], [[John McCarthy (computer scientist)|John McCarthy]] felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem solving, regardless of whether people used the same algorithms.<ref>

See [http://books.google.com/books?id=PEkqAAAAMAAJ&q=%22we+don't+care+if+it's+psychologically+real%22&dq=%22we+don't+care+if+it's+psychologically+real%22&output=html&pgis=1 Science at Google Books],
and [http://www.engagingexperience.com/2006/07/ai50_ai_past_pr.html McCarthy's presentation at AI@50]
</ref> His laboratory at [[Stanford University|Stanford]] ([[Stanford Artificial Intelligence Laboratory|SAIL]]) focused on using formal [[logic]] to solve a wide variety of problems, including [[knowledge representation]], [[automated planning and scheduling|planning]] and [[machine learning|learning]]. Work in logic led to the development of the programming language [[Prolog]] and the science of [[logic programming]].<ref>

{{Harvnb|Crevier|1993|pp=193-196}}

</ref>

; "Scruffy" symbolic AI:Researchers at [[MIT]] (such as [[Marvin Minsky]] and [[Seymour Papert]]) found that solving difficult problems in [[computer vision|vision]] and [[natural language processing]] required ad-hoc solutions &ndash; they argued that there was no [[silver bullet|easy answer]], no simple and general principle (like [[logic]]) that would capture all the aspects of intelligent behavior. [[Roger Schank]] described their "anti-logic" approaches as "[[Neats vs. scruffies|scruffy]]" (as opposed to the "[[Neats vs. scruffies|neat]]" paradigms at [[CMU]] and [[Stanford]]),<ref>
{{Harvnb|Crevier|1993|pp=163-176}}. [[Neats vs. scruffies]]: {{Harvnb|Crevier|1993|pp=168}}.
</ref> and this still forms the basis of research into [[commonsense knowledge bases]] (such as [[Doug Lenat]]'s [[Cyc]]) which must be built one complicated concept at a time.

; Knowledge based AI: When computers with large memories became available around 1970, researchers from all three traditions began to build [[knowledge representation|knowledge]] into AI applications. This "knowledge revolution" led to the development and deployment of [[expert system]]s (introduced by [[Edward Feigenbaum]]), the first truly successful form of AI software.<ref>{{Harvnb|Crevier|1993|pp=145-162}}</ref> The knowledge revolution was also driven by the realization that truly enormous amounts of knowledge would be required by many simple AI applications.

==== Sub-symbolic AI ====

During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on [[cybernetics]] or [[neural network]]s were abandoned or pushed into the background.<ref>
The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of [[perceptron]]s by [[Marvin Minsky]] and [[Seymour Papert]] in 1969. See [[History of AI]], [[AI winter]], or [[Frank Rosenblatt]]. {{Harv|Crevier|1993|pp=102-105}}.
</ref>
By the 1980s, however, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially [[machine perception|perception]], [[robotics]], [[machine learning|learning]] and [[pattern recognition]]. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.<ref>{{Harvtxt|Nilsson|1998|p=7}} characterizes these newer approaches to AI as "sub-symbolic".</ref>

; Bottom-up, situated, behavior based or nouvelle AI:Researchers from the related field of [[robotics]], such as [[Rodney Brooks]], rejected symbolic AI and focussed on the basic engineering problems that would allow robots to move and survive.<ref>{{Harvnb|Brooks|1990}} and {{Harvnb|Moravec|1988}}</ref> Their work revived the non-symbolic viewpoint of the early [[cybernetic]]s researchers of the 50s and reintroduced the use of [[control theory]] in AI. These approaches are also conceptually related to the [[embodied mind thesis]].

; Computational Intelligence:Interest in [[neural networks]] and "[[connectionism]]" was revived by [[David Rumelhart]] and others in the middle 1980s.<ref>

{{Harvnb|Crevier|1993|pp=214-215}} and {{Harvnb|Russell|Norvig|2003|p=25}}</ref> These and other sub-symbolic approaches, such as [[fuzzy system]]s and [[evolutionary computation]], are now studied collectively by the emerging discipline of [[computational intelligence]].<ref>
See [http://www.ieee-cis.org/ IEEE Computational Intelligence Society]
</ref>

; The new neats:In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools are truly [[scientific method|scientific]], in the sense that their results are both measurable and verifiable, and they have been responsible for many of AI's recent successes. The shared mathematical language has also permitted a high level of collaboration with more established fields (like [[mathematics]], [[economics]] or [[operations research]]). {{Harvtxt|Russell|Norvig|2003}} describe this movement as nothing less than a "revolution" and "the victory of the [[neats and scruffies|neats]]."<ref>

{{Harvnb|Russell|Norvig|2003|p=25-26}}

</ref>

==== Intelligent agent paradigm ====

The "[[intelligent agent]]" [[paradigm]] became widely accepted during the 1990s.<ref>
"The whole-agent view is now widely accepted in the field" {{Harvnb|Russell|Norvig|2003|p=55}}.
</ref>
An [[intelligent agent]] is a system that perceives its [[agent environment|environment]] and takes actions which maximizes its chances of success. The simplest intelligent agents are programs that solve specific problems. The most complicated intelligent agents are rational, thinking human beings.<ref name="IA">
The [[intelligent agent]] paradigm is discussed in major AI textbooks, such as: {{Harvnb|Russell|Norvig|2003|pp=27, 32-58, 968-972}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=7-21}}, {{Harvnb|Luger|Stubblefield|2004|pp=235-240}}
</ref>
The paradigm gives researchers license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on one single approach. An agent that solves a specific problem can use any approach that works — some agents are symbolic and logical, some are sub-symbolic [[neural network]]s and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as [[decision theory]] and [[economics]]—that also use concepts of abstract agents.

==== Integrating the approaches ====

An [[agent architecture]] or [[cognitive architecture]] allows researchers to build more versatile and intelligent systems out of interacting [[intelligent agents]] in a [[multi-agent system]].<ref>
[[Agent architecture]]s, [[hybrid intelligent system]]s, and [[multi-agent system]]s:

{{Harvnb|ACM|1998|loc=I.2.11}},

{{Harvtxt|Russell|Norvig|1998|pp=27, 932, 970-972}} and

{{Harvtxt|Nilsson|1998|loc=chpt. 25}}

</ref>
A system with both symbolic and sub-symbolic components is a [[hybrid intelligent system]], and the study of such systems is [[artificial intelligence systems integration]]. A [[hierarchical control system]] provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling.<ref>Albus, J. S. [http://www.isd.mel.nist.gov/documents/albus/4DRCS.pdf 4-D/RCS reference model architecture for unmanned ground vehicles.] In G Gerhart, R Gunderson, and C Shoemaker, editors, Proceedings of the SPIE AeroSense Session on Unmanned Ground Vehicle Technology, volume 3693, pages 11&mdash;20</ref> [[Rodney Brooks]]' [[subsumption architecture]] was an early proposal for such a hierarchical system.
<!--
This should also describe [[Soar (cognitive architecture)]] briefly.
--->

=== Tools of AI research ===

In the course of 50 years of research, AI has developed a large number of tools to solve the most difficult problems in [[computer science]]. A few of the most general of these methods are discussed below.

==== Search ====
{{Main|search algorithm}}

Many problems in AI can be solved in theory by intelligently searching through many possible solutions:<ref>[[Search algorithm]]s: {{Harvnb|Russell|Norvig|2003|pp=59-189}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=113-163}}, {{Harvnb|Luger|Stubblefield|2004|pp=79-164, 193-219}}, {{Harvnb|Nilsson|1998|loc=chpt. 7-12}}</ref> [[:#Deduction, reasoning, problem solving|Reasoning]] can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from [[premise]]s to [[conclusion]]s, where each step is the application of an [[inference rule]].<ref name="FC">[[Forward chaining]], [[backward chaining]], [[Horn clause]]s, and logical deduction as search: {{Harvnb|Russell|Norvig|2003|pp=217-225, 280-294}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=~46-52}}, {{Harvnb|Luger|Stubblefield|2004|pp=62-73}}, {{Harvnb|Nilsson|1998|loc=chpt. 4.2, 7.2}}</ref>
[[Automated planning and scheduling|Planning]] algorithms search through trees of goals and subgoals, attempting to find a path to a target goal.<ref>[[State space search]] and [[automated planning and scheduling|planning]]: {{Harvnb|Russell|Norvig|2003|pp=382-387}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=298-305}}, {{Harvnb|Nilsson|1998|loc=chpt. 10.1-2}}</ref> [[Robotics]] algorithms for moving limbs and grasping objects use [[local search (optimization)|local searches]] in [[configuration space]].<ref name="CS" /> Many [[machine learning|learning]] algorithms have search at their core.<ref name="DT" /> 

There are several types of search algorithms:
* "Uninformed" search algorithms eventually search through every possible answer until they locate their goal.<ref>Naive searches ([[breadth first search]], [[depth first search]] and general [[state space search]]): {{Harvnb|Russell|Norvig|2003|pp=59-93}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=113-132}}, {{Harvnb|Luger|Stubblefield|2004|pp=79-121}}, {{Harvnb|Nilsson|1998|loc=chpt. 8}}</ref> Naive algorithms quickly run into problems when they expand the size of their [[search space]] to [[astronomical]] numbers. The result is a search that is [[Computation time|too slow]] or never completes.
* [[Heuristic]] or "informed" searches use heuristic methods to eliminate choices that are unlikely to lead to their goal, thus drastically reducing the number of possibilities they must explore.<ref>[[Heuristic]] or informed searches (e.g., greedy [[best-first search|best first]] and [[A*]]): {{Harvnb|Russell|Norvig|2003|pp= 94-109}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=pp. 132-147}}, {{Harvnb|Luger|Stubblefield|2004|pp= 133-150}}, {{Harvnb|Nilsson|1998|loc=chpt. 9}}</ref> The eliminatation of choices that are certain not to lead to the goal is called [[pruning (algorithm)|pruning]].
* [[Local search (optimization)|Local searches]], such as [[hill climbing]], [[simulated annealing]] and [[beam search]], use techniques borrowed from [[optimization (mathematics)|optimization theory]].<ref>[[optimization (mathematics)|Optimization]] searches: {{Harvnb|Russell|Norvig|2003|pp=110-116,120-129}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=56-163}}, {{Harvnb|Luger|Stubblefield|2004|pp= 127-133}}</ref>
* [[Global optimization|Global searches]] are more robust in the presence of [[local optima]]. Techniques include [[evolutionary algorithms]], [[swarm intelligence]] and [[random optimization]] algorithms.

==== Logic ====
{{Main|logic programming|automated reasoning}}

[[Logic]]<ref>
[[Logic]]:

{{Harvnb|ACM|1998|loc=~I.2.3}},

{{Harvnb|Russell|Norvig|2003|pp=194-310}},

{{Harvnb|Luger|Stubblefield|2004|pp=35-77}},

{{Harvnb|Nilsson|1998|loc=chpt. 13-16}}

</ref>
was introduced into AI research by [[John McCarthy (computer scientist)|John McCarthy]] in his 1958 [[Advice Taker]] proposal. The most important technical development was [[J. Alan Robinson]]'s discovery of the [[resolution (logic)|resolution]] and [[unification]] algorithm for logical deduction in 1963. This procedure is simple, complete and entirely algorithmic, and can easily be performed by digital computers.<ref>
[[Resolution (logic)|Resolution]] and [[unification]]:

{{Harvnb|Russell|Norvig|2003|pp=213-217, 275-280, 295-306}},

{{Harvnb|Poole|Mackworth|Goebel|1998|pp=56-58}},

{{Harvnb|Luger|Stubblefield|2004|pp=554-575}},

{{Harvnb|Nilsson|1998|loc=chpt. 14 & 16}}

</ref> However, a naive implementation of the algorithm quickly leads to a [[combinatorial explosion]] or an [[infinite loop]]. In 1974, [[Robert Kowalski]] suggested representing logical expressions as [[Horn clauses]] (statements in the form of rules: "if ''p'' then ''q''"), which reduced logical deduction to [[backward chaining]] or [[forward chaining]]. This greatly alleviated (but did not eliminate) the problem.<ref name="FC" /><ref name="HLP">
History of logic programming:

{{Harvnb|Crevier|1993|pp=190-196}}.
Advice Taker:

{{Harvnb|McCorduck|2004|p=51}},

{{Harvnb|Russell|Norvig|2003|pp=19}}

</ref>

Logic is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the [[satplan]] algorithm uses logic for [[automated planning and scheduling|planning]],<ref>
[[Satplan]]:

{{Harvnb|Russell|Norvig|2003|pp=402-407}},

{{Harvnb|Poole|Mackworth|Goebel|1998|pp=300-301}},

{{Harvnb|Nilsson|1998|loc=chpt. 21}}

</ref>
and [[inductive logic programming]] is a method for [[machine learning|learning]].<ref>
[[Explanation based learning]], [[relevance based learning]], [[inductive logic programming]], [[case based reasoning]]:

{{Harvnb|Russell|Norvig|2003|pp=678-710}},

{{Harvnb|Poole|Mackworth|Goebel|1998|pp=414-416}},

{{Harvnb|Luger|Stubblefield|2004|pp=~422-442}},

{{Harvnb|Nilsson|1998|loc=chpt. 10.3, 17.5}}

</ref>

There are several different forms of logic used in AI research.

* [[Propositional logic]]<ref>

[[Propositional logic]]:

{{Harvnb|Russell|Norvig|2003|pp=204-233}},

{{Harvnb|Luger|Stubblefield|2004|pp=45-50}}

{{Harvnb|Nilsson|1998|loc=chpt. 13}}

</ref> or [[sentential logic]] is the logic of statements which can be true or false.

* [[First-order logic]]<ref>

[[First-order logic]] and features such as [[equality]]:

{{Harvnb|ACM|1998|loc=~I.2.4}},

{{Harvnb|Russell|Norvig|2003|pp=240-310}},

{{Harvnb|Poole|Mackworth|Goebel|1998|pp=268-275}},

{{Harvnb|Luger|Stubblefield|2004|pp=50-62}},

{{Harvnb|Nilsson|1998|loc=chpt. 15}}

</ref> also allows the use of [[quantifier]]s and [[predicate]]s, and can express facts about objects, their properties, and their relations with each other.

* [[Fuzzy logic]], a version of first-order logic which allows the truth of a statement to be represented as a value between 0 and 1, rather than simply True (1) or False (0). [[Fuzzy system]]s can be used for uncertain reasoning and have been widely used in modern industrial and consumer product control systems.<ref name="FL">

[[Fuzzy logic]]:

{{Harvnb|Russell|Norvig|2003|pp=526-527}}

</ref>

* [[Default logic]]s, [[non-monotonic logic]]s and [[circumscription]] are forms of logic designed to help with default reasoning and the [[qualification problem]].<ref name="NML" />
* Several extensions of logic have been designed to handle specific domains of [[knowledge representation|knowledge]], such as: [[description logic]]s;<ref name="DL" /> [[situation calculus]], [[event calculus]] and [[fluent calculus]] (for representing events and time);<ref name="SC" /> [[Causality#causal calculus|causal calculus]];<ref name="CC" /> [[belief calculus]]; and [[modal logic]]s.<ref name="BC" />

====Probabilistic methods for uncertain reasoning====
{{Main|Bayesian network|hidden Markov model|Kalman filter|decision theory|utility theory}}
<!-- THIS SECTION NEEDS A REWRITE -->
Many problems in AI (in reasoning, planning, learning, perception and robotics) require the agent to operate with incomplete or uncertain information. Starting in the late 80s and early 90s, [[Judea Pearl]] and others championed the use of methods drawn from [[probability]] theory and [[economics]] to devise a number of powerful tools to solve these problems.<ref>{{Harvnb|Russell|Norvig|2003|pp=25-26}} (on [[Judea Pearl]]'s contribution).
Stochastic methods are described in all the major AI textbooks:
{{Harvnb|ACM|1998|loc=~I.2.3}},
{{Harvnb|Russell|Norvig|2003|pp=462-644}},
{{Harvnb|Poole|Mackworth|Goebel|1998|pp=345-395}},
{{Harvnb|Luger|Stubblefield|2004|pp=165-191, 333-381}},
{{Harvnb|Nilsson|1998|loc=chpt. 19}}
</ref>

[[Bayesian network]]s<ref>[[Bayesian network]]s: {{Harvnb|Russell|Norvig|2003|pp=492-523}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=361-381}}, {{Harvnb|Luger|Stubblefield|2004|pp=~182-190, ~363-379}}, {{Harvnb|Nilsson|1998|loc=chpt. 19.3-4}}</ref>
are very general tool that can be used for a large number of problems: reasoning (using the [[Bayesian inference]] algorithm),<ref>
[[Bayesian inference]] algorithm: {{Harvnb|Russell|Norvig|2003|pp=504-519}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=361-381}}, {{Harvnb|Luger|Stubblefield|2004|pp=~363-379}}, {{Harvnb|Nilsson|1998|loc=chpt. 19.4 & 7}}</ref>
[[Machine learning|learning]] (using the [[expectation-maximization algorithm]]),<ref name="BL">
[[Bayesian]] learning and the [[expectation-maximization algorithm]]: {{Harvnb|Russell|Norvig|2003|pp=712-724}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=424-433}}, {{Harvnb|Nilsson|1998|loc=chpt. 20}}</ref>
[[Automated planning and scheduling|planning]] (using [[decision network]]s)<ref name="BDN">[[Bayesian]] [[decision network]]s: {{Harvnb|Russell|Norvig|2003|pp=597-600}}</ref>
and [[machine perception|perception]] (using [[dynamic Bayesian network]]s).<ref>[[Dynamic Bayesian network]]: {{Harvnb|Russell|Norvig|2003|pp=551-557}}</ref>

Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping [[machine perception|perception]] systems to analyze processes that occur over time<ref>{{Harvnb|Russell|Norvig|2003|pp=537-581}}</ref>
(e.g., [[hidden Markov model]]s<ref>[[Hidden Markov model]]: {{Harvnb|Russell|Norvig|2003|pp=549-551}}</ref>
and [[Kalman filter]]s<ref>[[Kalman filter]]: {{Harvnb|Russell|Norvig|2003|pp=551-557}}</ref>).

Planning problems have also taken advantages of other tools from economics, such as [[decision theory]] and [[decision analysis]],<ref name="DTA">[[decision theory]] and [[decision analysis]]: {{Harvnb|Russell|Norvig|2003|pp=584-597}}, {{Harvnb|Poole|Mackworth|Goebel|1998|pp=381-394}}</ref>
[[applied information economics|information value theory]],<ref name="IVT">[[Applied information economics|Information value theory]]: {{Harvnb|Russell|Norvig|2003|pp=600-604}}</ref>
[[Markov decision process]]es,<ref name="MDPDDN" >[[Markov decision process]]es and dynamic [[decision network]]s:{{Harvnb|Russell|Norvig|2003|pp=613-631}}</ref>
dynamic [[decision network]]s,<ref name="MDPDDN" />
[[game theory]] and [[mechanism design]]<ref name="GTMD">[[Game theory]] and [[mechanism design]]:
{{Harvnb|Russell|Norvig|2003|pp=631-643}}</ref>

==== Classifiers and statistical learning methods ====
{{Main|classifier (mathematics)|statistical classification|machine learning}}

The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do however also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems.

[[Classifier (mathematics)|Classifiers]]<ref>
Statistical learning methods and [[Classifier (mathematics)|classifiers]]:

{{Harvnb|Russell|Norvig|2003|pp=712-754}},

{{Harvnb|Luger|Stubblefield|2004|pp=453-541}}

</ref> are functions that use [[pattern matching]] to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set.

When a new observation is received, that observation is classified based on previous experience. A classifier can be trained in various ways; there are many statistical and [[machine learning]] approaches.

A wide range of classifiers are available, each with its strengths and weaknesses. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the "no free lunch" theorem. Various empirical tests have been performed to compare classifier performance and to find the characteristics of data that determine classifier performance. Determining a suitable classifier for a given problem is however still more an art than science.

The most widely used classifiers are the [[Artificial neural network|neural network]],<ref name="NN" />
[[kernel methods]] such as the [[support vector machine]],<ref>
[[Kernel methods]]:

{{Harvnb|Russell|Norvig|2003|pp=749-752}}

</ref>
[[k-nearest neighbor algorithm]],<ref>
[[K-nearest neighbor algorithm]]:

{{Harvnb|Russell|Norvig|2003|pp=733-736}}

</ref> [[Gaussian mixture model]],<ref>
[[Gaussian mixture model]]:

{{Harvnb|Russell|Norvig|2003|pp=725-727}}

</ref>
[[naive Bayes classifier]],<ref>
[[Naive Bayes classifier]]:

{{Harvnb|Russell|Norvig|2003|pp=718}}

</ref>
and [[decision tree]].<ref name="DT">
[[Alternating decision tree|Decision tree]]:

{{Harvnb|Russell|Norvig|2003|pp=653-664}},

{{Harvnb|Poole|Mackworth|Goebel|1998|pp=403-408}},

{{Harvnb|Luger|Stubblefield|2004|pp=408-417}}

</ref>
The performance of these classifiers have been compared over a wide range of classification tasks<ref>

{{cite-web| last=van der Walt | first=Christiaan | url=http://www.patternrecognition.co.za/publications/cvdwalt_data_characteristics_classifiers.pdf|title= Data characteristics that determine classifier performance}}</ref>
in order to find data characteristics that determine classifier performance.

==== Neural networks ====
{{main|neural networks|connectionism}}

[[Image:Artificial neural network.svg|thumb|180px|A neural network is an interconnected group of nodes, akin to the vast network of [[neuron]]s in the [[human brain]].]]

The study of [[artificial neural network]]s<ref name="NN">Neural networks and connectionism:

{{Harvnb|Russell|Norvig|2003|pp=736-748}},
{{Harvnb|Poole|Mackworth|Goebel|1998|pp=408-414}},
{{Harvnb|Luger|Stubblefield|2004|pp=453-505}},
{{Harvnb|Nilsson|1998|loc=chpt. 3}}

</ref> began with [[cybernetic]]s researchers, working in the decade before the field AI research was founded. In the 1960s [[Frank Rosenblatt]] developed an important early version, the [[perceptron]].<ref>[[Perceptrons]]:
{{Harvnb|Russell|Norvig|2003|pp=740-743}},
{{Harvnb|Luger|Stubblefield|2004|pp=458-467}}</ref>

[[Paul Werbos]] developed the [[backpropagation]] algorithm for [[multilayer perceptron]]s in 1974,<ref>[[Backpropagation]]:

{{Harvnb|Russell|Norvig|2003|pp=744-748}},
{{Harvnb|Luger|Stubblefield|2004|pp=467-474}},
{{Harvnb|Nilsson|1998|loc=chpt. 3.3}}

</ref> which led to a renaissance in neural network research and [[connectionism]] in general in the middle 1980s. Other common network architectures which have been developed include the [[feedforward neural network]], the [[radial basis network]], the Kohonen [[self-organizing map]] and various [[recurrent neural network]]s. The [[Hopfield net]], a form of attractor network, was first described by [[John Hopfield]] in 1982.

Neural networks are applied to the problem of [[machine learning|learning]], using such techniques as [[Hebbian learning]]<ref name="LS474">, 
[[Competitive learning]], [[Hebbian theory|Hebbian]]  coincidence learning, [[Hopfield network]]s and attractor networks:

{{Harvnb|Luger|Stubblefield|2004|pp=474-505}}.
</ref>
, [[Holographic associative memory]] and the relatively new field of [[Hierarchical Temporal Memory]] which simulates the architecture of the [[neocortex]].<ref>

{{Harvnb|Hawkins|Blakeslee|2004}}

</ref>

==== Social and emergent models ====
{{Main|evolutionary computation}}

Several algorithms for [[machine learning|learning]] use tools from [[evolutionary computation]], such as [[genetic algorithms]],<ref>{{cite book |last=Holland |first=John H. |year=1975 |title=Adaptation in Natural and Artificial Systems | publisher=University of Michigan Press | isbn = 0262581116}}</ref><ref>
[[Genetic algorithm]]s for learning:

{{Harvnb|Luger|Stubblefield|2004|pp=509-530}},

{{Harvnb|Nilsson|1998|loc=chpt. 4.2}}

</ref> [[swarm intelligence]].<ref>
[[Artificial life]] and society based learning:
{{Harvnb|Luger|Stubblefield|2004|pp=530-541}}
</ref>
and [[genetic programming]].<ref>{{cite book |last=Koza|first=John R. |year=1992 |title=Genetic Programming| subtitle=On the Programming of Computers by Means of Natural Selection | publisher=MIT Press}}</ref><ref>{{cite book | author=Poli, R., Langdon, W. B., McPhee, N. F. |year=2008 |title=A Field Guide to Genetic Programming | publisher=Lulu.com, freely available from http://www.gp-field-guide.org.uk/  | isbn = 978-1-4092-0073-4}}</ref>

==== Control theory ====
{{Main|intelligent control}}

[[Control theory]], the grandchild of [[cybernetics]], has many important applications, especially in [[robotics]].<ref>
[[Control theory]]:

{{Harvnb|ACM|1998|loc=~I.2.8}},

{{Harvnb|Russell|Norvig|2003|pp=926-932}}

</ref>

==== Specialized languages ====

AI researchers have developed several specialized languages for AI research:

* [[Information Processing Language|IPL]], one of the first programming languages, developed by [[Alan Newell]], [[Herbert Simon]] and [[J. C. Shaw]].<ref>

{{Harvnb|Crevier|1993|p=46-48}}

</ref>

* [[Lisp programming language|Lisp]]<ref>

[[Lisp (programming language)|Lisp]]:

{{Harvnb|Luger|Stubblefield|2004|pp=723-821}}

</ref> was developed by [[John McCarthy (computer scientist)|John McCarthy]] at [[MIT]] in 1958.<ref>

{{Harvnb|Crevier|1993|pp=59-62}},

{{Harvnb|Russell|Norvig|2003|p=18}}

</ref> There are many dialects of Lisp in use today.

* [[Prolog]],<ref>

[[Prolog]]:

{{Harvnb|Poole|Mackworth|Goebel|1998|pp=477-491}},

{{Harvnb|Luger|Stubblefield|2004|pp=641-676, 575-581}}

</ref> a language based on [[logic programming]], was invented by [[France|French]] researchers [[Alain Colmerauer]] and [[Phillipe Roussel]], in collaboration with [[Robert Kowalski]] of the [[University of Edinburgh]].<ref name="HLP" />

* [[STRIPS]], a planning language developed at [[Stanford]] in the 1960s.
* [[Planner (programming language)|Planner]] developed at [[MIT]] around the same time.

AI applications are also often written in standard languages like [[C++]] and languages designed for mathematics, such as [[Matlab]] and [[Lush (programming language)|Lush]].

=== Evaluating artificial intelligence ===
{{main|Progress in artificial intelligence}}
How can one determine if an agent is intelligent? In 1950, Alan Turing proposed a general procedure to test the intelligence of an agent now known as the [[Turing test]]. This procedure allows almost all the major problems of artificial intelligence to be tested. However, it is a very difficult challenge and at present all agents fail.

Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing recognition and game-playing. Such tests have been termed [[subject matter expert Turing test]]s. Smaller problems provide more achievable goals and there are an ever-increasing number of positive results.

The broad classes of outcome for an AI test are:

* '''optimal''': it is not possible to perform better
* '''strong super-human''': performs better than all humans
* '''super-human''': performs better than most humans
* '''sub-human''': performs worse than most humans

For example, performance at checkers ([[draughts]]) is optimal,<ref>{{cite web | url=http://www.sciencemag.org/cgi/content/abstract/1144079 | title=Checkers Is Solved | date=2007-07-19 | accessdate=2007-07-20 | publisher=Science | first=Jonathan | last=Schaeffer}}</ref> performance at chess is super-human and nearing strong super-human,<ref>[[Computer Chess#Computers versus humans]]</ref> and performance at many everyday tasks performed by humans is sub-human.

=== Competitions and prizes ===
{{main|Competitions and prizes in artificial intelligence}}
There are a number of competitions and prizes to promote research in artificial intelligence. The main areas promoted are: general machine intelligence, conversational behaviour, data-mining, driverless cars, robot soccer and games.

== Applications of artificial intelligence ==
{{main|Applications of artificial intelligence}}

Artificial intelligence has successfully been used in a wide range of fields including [[medical diagnosis]], [[stock trading]], [[robot control]], [[law]], scientific discovery and toys. Frequently, when a technique reaches mainstream use it is no longer considered artificial intelligence, sometimes described as the [[AI effect]].<ref>{{cite web
  | last = 
  | first = 
  | authorlink = 
  | coauthors = 
  | title = AI set to exceed human brain power
  | work = 
  | publisher = CNN.com
  | date = 2006-07-26
  | url = http://www.cnn.com/2006/TECH/science/07/24/ai.bostrom/
  | format = web article
  | doi = 
  | accessdate = 2008-02-26}}</ref> It may also become integrated into [[artificial life]].

== See also ==

* [[List of basic artificial intelligence topics]]
* [[:Category:Artificial intelligence researchers|List of AI researchers]]
* [[List of notable artificial intelligence projects|List of AI projects]]
* [[List of important publications in computer science#Artificial intelligence|List of important AI publications]]
* [[List of emerging technologies]]
<!-- This is not a list of your pet website or article, or favorite AI software & books. please add those to the appropriate links in the see also section. Keep this list short and use only famous and clear examples -->

== Notes ==
{{reflist|3}}

== References ==
=== Major AI textbooks ===

* {{Citation|first=George|last= Luger |first2=William|last2= Stubblefield|year=2004|title=Artificial Intelligence: Structures and Strategies for Complex Problem Solving|edition=5th|publisher=The Benjamin/Cummings Publishing Company, Inc.|pages= 720|isbn=0-8053-4780-1|url=http://www.cs.unm.edu/~luger/ai-final/tocfull.html}}
* {{Citation|last=Nilsson|first=Nils|author-link=Nils Nilsson|year=1998|title=Artificial Intelligence: A New Synthesis|publisher=Morgan Kaufmann Publishers|isbn=978-1-55860-467-4}}
* {{Russell Norvig 2003}}
* {{Citation | first = David | last = Poole | first2 = Alan | last2 = Mackworth | first3 = Randy | last3 = Goebel | publisher = Oxford University Press | publisher-place = New York | year = 1998 | title = Computational Intelligence: A Logical Approach | url = http://www.cs.ubc.ca/spider/poole/ci.html | author-link=David Poole }}

=== Other sources ===

* {{Citation| last=ACM | first=(Association of Computing Machinery) |year=1998|title=ACM Computing Classification System: Artificial intelligence|url=http://www.acm.org/class/1998/I.2.html |author-link=Association of Computing Machinery}}
* {{Citation | last=Brooks | first=Rodney | author-link=Rodney Brooks | year =1990 | title = Elephants Don't Play Chess | journal = Robotics and Autonomous Systems | volume=6 | pages=3-15 | url=http://people.csail.mit.edu/brooks/papers/elephants.pdf | accessdate=2007-08-30}}
* {{Citation | last=Buchanan | first = Bruce G. | year= 2005 | title = A (Very) Brief History of Artificial Intelligence | magazine = AI Magazine <!-- WINTER -->| pages=53-60 | url=http://www.aaai.org/AITopics/assets/PDF/AIMag26-04-016.pdf | accessdate=2007-08-30 }}
* {{Crevier 1993}}
* {{Citation | last=Haugeland | first=John | author-link = John Haugeland | year = 1985 | title = Artificial Intelligence: The Very Idea | publisher=MIT Press| location= Cambridge, Mass. | isbn=0-262-08153-9 }}.
* {{Citation | last=Hawkins | first=Jeff | author-link=Jeff Hawkins | last2=Blakeslee | first2=Sandra | year=2004 | title=On Intelligence | publisher=Owl Books | location=New York, NY | isbn=0-8050-7853-3 }}.
* {{Citation | last=Kahneman | first=Daniel | author-link=Daniel Kahneman | last2=Slovic | first2= D. | last3=Tversky | first3=Amos | author3-link=Amos Tversky | year=1982 | title=Judgment under uncertainty: Heuristics and biases | location=New York |publisher=Cambridge University Press}}.
* {{Citation | last=Kurzweil | first=Ray | author-link=Ray Kurzweil | year=1999 | title=The Age of Spiritual Machines | publisher=Penguin Books | isbn=0-670-88217-8 }}
* {{Citation | last=Kurzweil | first=Ray | author-link=Ray Kurzweil | year=2005 | title=The Singularity is Near | publisher=Penguin Books | isbn=0-670-03384-7 }}
* {{Citation | last=Lakoff | first=George | author-link=George Lakoff | last2=Núñez | first2=Rafael E. | author2-link=Rafael Núñez | year=2000 | title=[[Where Mathematics Comes From|Where Mathematics Comes From: How the Embodied Mind Brings Mathematics into Being]] | publisher=Basic Books | isbn= 0-465-03771-2}}.
* {{Citation | last=Lenat | first = Douglas | year = 1989 | title = Building Large Knowledge-Based Systems | publisher = Addison-Wesley| author-link=Douglas Lenat }}
* {{Citation | last=Lighthill | first = Professor Sir James | year = 1973 | contribution= Artificial Intelligence: A General Survey | title = Artificial Intelligence: a paper symposium| publisher = Science Research Council|author-link=James Lighthill }}
* {{Citation | last=McCarthy | first=John | last2 = Minsky | first2 = Marvin | last3 = Rochester | first3 = Nathan | last4 = Shannon | first4 = Claude | url = http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html | title = A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence | year = 1955 | author-link = John McCarthy (computer scientist) | author2-link = Marvin Minsky | author3-link = Nathan Rochester | author4-link = Claude Shannon}}.
* {{Citation | last=McCarthy | first=John | last2 = Hayes | first2=P. J. | year = 1969 | url=http://www-formal.stanford.edu/jmc/mcchay69.html | title= Some philosophical problems from the standpoint of artificial intelligence | journal =Machine Intelligence | volume= 4 | pages = 463-502 | author-link = John McCarthy (computer scientist) }}
* {{Citation | last=McCorduck | first=Pamela | year = 2004 | title = Machines Who Think | publisher=A. K. Peters, Ltd. | location=Natick, MA | edition=2nd | isbn=1-56881-205-1}}.
* {{Citation | last=Minsky | first=Marvin | author-link=Marvin Minsky | year = 1967 | title = Computation: Finite and Infinite Machines | publisher = Prentice-Hall | location=Englewood Cliffs, N.J. }}
* {{Citation | last=Minsky | first=Marvin | author-link=Marvin Minsky | year = 2006 | title = The Emotion Machine | publisher = Simon & Schusterl | publication-place=New York, NY | isbn=0-7432-7663-9 }}
* {{Citation | last=Moravec | first=Hans | year = 1976 | url= http://www.frc.ri.cmu.edu/users/hpm/project.archive/general.articles/1975/Raw.Power.html | title = The Role of Raw Power in Intelligence | author-link=Hans Moravec }}
* {{Citation | last=Moravec | first=Hans | year = 1988 | title = Mind Children | publisher = Harvard University Press | author-link =Hans Moravec }}
* {{Citation | last=NRC |chapter=Developments in Artificial Intelligence|chapter-url=http://www.nap.edu/readingroom/books/far/ch9.html|title=Funding a Revolution: Government Support for Computing Research|publisher=National Academy Press|year=1999| author-link=United States National Research Council | access-date=30 August 2007}}
* {{Citation | last=Newell | first = Allen | last2 = Simon | first2=H. A. | year = 1963 | contribution=GPS: A Program that Simulates Human Thought| title=Computers and Thought | editor-last= Feigenbaum | editor-first= E.A. |editor2-last= Feldman |editor2-first= J. |publisher= McGraw-Hill|publisher-place= New York | author-link=Allen Newell|author2-link=Herbert Simon}}
* {{Citation | last=Searle | first = John | author-link=John Searle | year = 1980 | url = http://www.bbsonline.org/documents/a/00/00/04/84/bbs00000484-00/bbs.searle2.html | title = Minds, Brains and Programs | journal = Behavioral and Brain Sciences | volume = 3| issue = 3| pages= 417-457}}
* {{Citation | last=Shapiro| first= Stuart C. | year=1992 | url=http://www.cse.buffalo.edu/~shapiro/Papers/ai.ps | contribution =Artificial Intelligence | editor-first=Stuart C. | editor-last=Shapiro | title=Encyclopedia of Artificial Intelligence |edition=2nd |pages=54-57| location=New York |publisher= John Wiley}}.
* {{Citation | last=Simon | first = H. A. | year = 1965 | title=The Shape of Automation for Men and Management | publisher =Harper & Row | publication-place = New York | author-link=Herbert Simon}}
* {{Citation | last=Turing | first = Alan | year=1950 | title = [[Computing machinery and intelligence]] | journal=Mind | issn=0026-4423 | volume = LIX | issue = 236 |date=October 1950 | pages= 433-460 | url =http://loebner.net/Prizef/TuringArticle.html | authorlink = Alan Turing | doi=10.1093/mind/LIX.236.433}}
* {{Citation | last=Wason | first=P. C. | author-link=Peter Cathcart Wason | coauthors=Shapiro, D. | editor=Foss, B. M. | title=New horizons in psychology | year=1966 | location=Harmondsworth | publisher=Penguin | chapter=Reasoning }}
* {{Citation | last=Weizenbaum | first = Joseph | title = Computer Power and Human Reason | publisher = W.H. Freeman & Company | location = San Francisco | year = 1976 | authorlink=Joseph Weizenbaum | isbn = 0716704641}}

== Further reading ==

* R. Sun & L. Bookman, (eds.), ''Computational Architectures: Integrating Neural and Symbolic Processes''. Kluwer Academic Publishers, Needham, MA. 1994.
* [[Margaret Boden]], Mind As Machine, [[Oxford University Press]], 2006

== External links ==
{{linkfarm}}
{{sisterlinks|Artificial Intelligence}}
* {{dmoz|Computers/Artificial_Intelligence/|AI}}
* [http://www.aaai.org/AITopics/html/welcome.html The Association for the Advancement of Artificial Intelligence]
* [http://www.vega.org.uk/video/programme/16 Freeview Video 'Machines with Minds' by the Vega Science Trust and the BBC/OU]
* [http://www-formal.stanford.edu/jmc/whatisai/whatisai.html John McCarthy's frequently asked questions about AI]
* [http://www.wfs.org/Dec-janfiles/AIInt.htm The Futurist magazine interviews "Ai chasers" Rodney Brooks, Peter Norvig, Barney Pell, et al.]
* [http://www.aiai.ed.ac.uk/events/jonathanedwards2007/bbc-r4-jonathan-edwards-2007-03-28.mp3 Jonathan Edwards looks at AI (BBC audio)]С
* [http://www.kurzweilai.net/ Ray Kurzweil's website dedicated to AI including prediction of future development in AI]
* {{sep entry|logic-ai|Logic and Artificial Intelligence|Richmond Thomason}}

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