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<!-- DEFINITIONS -->
'''Artificial intelligence''' ('''AI''') is the [[intelligence]] of machines and the branch of [[computer science]] that aims to create it. Textbooks define the field as "the study and design of [[intelligent agent]]s,"<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 that 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.
</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 (computer scientist)|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>

<!-- SUMMARIZING FICTION/SPECULATION, PHILOSOPHY, HISTORY  -->
The field was founded on the claim that a central property of humans, intelligence—the [[sapience]] of ''[[Homo sapiens]]''—can be so precisely described that it can be simulated by a machine.<ref>
See the [[Dartmouth conference|Dartmouth proposal]], under [[#Philosophy|Philosophy]], below.
</ref>
This raises philosophical issues about the nature of the [[mind]] and limits of scientific [[hubris]], issues which have been addressed by [[History of AI#AI in myth, fiction and speculation|myth]], [[artificial intelligence in fiction|fiction]] and [[philosophy of AI|philosophy]] since antiquity.<ref name="MCCORDUCK">
This is a central idea of [[Pamela McCorduck]]'s ''Machines That Think''. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition." {{Harv|McCorduck|2004|p=34}} "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized." {{Harv|McCorduck|2004|p=xviii}} "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction." {{Harv|McCorduck|2004|p=3}} She traces the desire back to its [[Hellenistic]] roots and calls it the urge to "forge the Gods." {{Harv|McCorduck|2004|p=340-400}}
</ref>
Artificial intelligence has been the subject of optimism,<ref>
The optimism referred to includes the predictions of early AI researchers (see [[History of AI#The optimism|optimism in the history of AI]]) as well as the ideas of modern [[transhumanism|transhumanists]] such as [[Ray Kurzweil]].
</ref>
but has also suffered setbacks<ref>
The "setbacks" referred to include the [[AI winter#Machine translation and the ALPAC report of 1966|ALPAC report]] of 1966, the abandonment of [[perceptrons]] in 1970, the [[AI winter#The Lighthill report|the Lighthill Report]] of 1973 and the [[AI winter#The collapse of the Lisp machine market in 1987|collapse of the lisp machine market]] in 1987.
</ref>
and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.<ref name="AI_APPS">
AI applications widely used behind the scenes:
* {{Harvnb|Russell|Norvig|2003|p=28}}
* {{Harvnb|Kurzweil|2005|p=265}}
* {{Harvnb|NRC|1999|pp=216–222}}
</ref>

<!-- SUMMARIZING PROBLEMS, APPROACHES and TOOLS -->
AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other.<ref name=FRAG>
Pamela {{Harvtxt|McCorduck|2004|pp=424}} writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other."
</ref>
Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, longstanding differences of opinion about how AI should be done and the application of widely differing tools. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to 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}}
* {{Harvnb|Nilsson|1998}}
</ref>
General intelligence (or "[[strong AI]]") is still a long-term goal of (some) research.<ref name="GI">
General intelligence ([[strong AI]]) is discussed in popular introductions to AI:
* {{Harvnb|Kurzweil|1999}} and {{Harvnb|Kurzweil|2005}}
</ref><br />
<!-- TODO: SUMMARIZING APPLICATIONS -->

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

<!-- AN ANCIENT DREAM (MCCORDUCK'S THESIS) -->
Thinking machines and artificial beings appear in [[Greek myth]]s, such as [[Talos]] of [[Crete]], the golden robots of [[Hephaestus]] and [[Pygmalion (mythology)|Pygmalion's]] [[Galatea (mythology)|Galatea]].<ref>
AI in Myth:
* {{Harvnb|McCorduck|2004|p=4-5}}
* {{Harvnb|Russell|Norvig|2003|p=939}}
</ref>
Human likenesses believed to have intelligence were built in every major civilization: animated [[cult image|statue]]s were worshipped in [[Egypt]] and [[Greece]]<ref>
[[cult image|Sacred statue]]s as artificial intelligence:
* {{Harvtxt|Crevier|1993|p=1}} (statue of [[Amun]])
* {{Harvtxt|McCorduck|2004|pp=6–9}}
These were the first machines to be believed to have true intelligence and consciousness. [[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 [[613 Commandments|Mosaic law]] (developed around the same time), which expressly forbids the worship of robots {{Harv|McCorduck|2004|pp=6–9}}
</ref>
and humanoid [[automaton]]s were built by [[King Mu of Zhou|Yan Shi]],<ref>
{{Harvnb|Needham|1986|p=53}}
</ref>
[[Hero of Alexandria]],<ref>
{{Harvnb|McCorduck|2004|p=6}}
</ref>
[[Al-Jazari]]<ref>{{cite web|url=http://www.shef.ac.uk/marcoms/eview/articles58/robot.html |title=A Thirteenth Century Programmable Robot |publisher=Shef.ac.uk |date= |accessdate=2009-04-25}}</ref>
and [[Wolfgang von Kempelen]].<ref>
{{Harvnb|McCorduck|2004|p=17}}
</ref>
It was also widely believed that artificial beings had been created by [[Jābir ibn Hayyān]],<ref>
[[Takwin]]:
{{cite document |author=O'Connor, Kathleen Malone |title=The alchemical creation of life (takwin) and other concepts of Genesis in medieval Islam|publisher=University of Pennsylvania |year=1994 |url=http://repository.upenn.edu/dissertations/AAI9503804 |accessdate=2007-01-10}}
</ref>
[[Judah Loew]]<ref>
[[Golem]]:
{{Harvnb|McCorduck|2004|p=15-16}}, {{Harvnb|Buchanan|2005|p=50}}
</ref>
and [[Paracelsus]].<ref>
{{Harvnb|McCorduck|2004|p=13-14}}
</ref>
By the 19th and 20th centuries, artificial beings had become a common feature in fiction, as in [[Mary Shelley]]'s ''[[Frankenstein]]'' or [[Karel Čapek]]'s ''[[R.U.R. (Rossum's Universal Robots)]]''.<!-- PLEASE DON'T ADD MORE EXAMPLES. THIS IS ENOUGH. SEE SECTION AT BOTTOM OF ARTICLE ON SPECULATION.--><ref>
{{Harvnb|McCorduck|2004|pp=17–25}}
</ref>
[[Pamela McCorduck]] argues that all of these are examples of an ancient urge, as she describes it, "to forge the gods".<ref name="MCCORDUCK"/> Stories of these creatures and their fates discuss many of the same hopes, fears and [[ethics of artificial intelligence|ethical concerns]] that are presented by artificial intelligence.

<!-- MAJOR INTELLECTUAL PRECURSORS: LOGIC, THEORY OF COMPUTATION, CYBERNETICS, INFORMATION THEORY, EARLY NEURAL NETS -->
Mechanical or [[formal reasoning|"formal" reasoning]] has been developed by philosophers and mathematicians since antiquity. The study of logic led directly to the invention of the [[computer|programmable digital electronic computer]], based on the work of [[mathematician]] [[Alan Turing]] and others. Turing's [[theory of computation]] suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction.<ref>
This insight, that digital computers can simulate any process of formal reasoning, is known as the [[Church-Turing thesis]].
</ref>
This, along with recent discoveries in [[neurology]], [[information theory]] and [[cybernetic]]s, inspired a small group of researchers to begin to seriously consider the possibility of building an electronic brain.<ref name="IMMEDIATE PRECURSORS">
AI's immediate precursors:
* {{Harvnb|McCorduck|2004|pp=51–107}}
* {{Harvnb|Crevier|1993|pp=27–32}}
* {{Harvnb|Russell|Norvig|2003|pp=15, 940}}
* {{Harvnb|Moravec|1988|p=3}}
See also {{See section|History of artificial intelligence|Cybernetics and early neural networks}}. Among the researchers who laid the foundations of AI were [[Alan Turing]], [[John Von Neumann]], [[Norbert Weiner]], [[Claude Shannon]], [[Warren McCullough]], [[Walter Pitts]] and [[Donald Hebb]].
</ref>

<!-- THE "GOLDEN YEARS" 1956-1974 -->
The field of AI research was founded at [[Dartmouth Conferences|a conference]] on the campus of [[Dartmouth College]] in the summer of 1956.<ref>
[[Dartmouth conference]]:
* {{Harvnb|McCorduck|2004|pp=111–136}}
* {{Harvnb|Crevier|1993|pp=47–49}}, who writes "the conference is generally recognized as the official birthdate of the new science."
* {{Harvnb|Russell|Norvig|2003|p=17}}, who call the conference "the birth of artifcial intelligence."
* {{Harvnb|NRC|1999|pp=200–201}}
</ref>
The attendees, including [[John McCarthy (computer scientist)|John McCarthy]], [[Marvin Minsky]], [[Allen Newell]] and [[Herbert Simon]], became the leaders of AI research for many decades.<ref>
Hegemony of the Dartmouth conference attendees:
* {{Harvnb|Russell|Norvig|2003|p=17}}, who write "for the next 20 years the field would be dominated by these people and their students."
* {{Harvnb|McCorduck|2004|pp=129–130}}
</ref>
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>
"[[History of AI#The golden years 1956−1974|Golden years]]" of AI (successful symbolic reasoning programs 1956-1973):
* {{Harvnb|McCorduck|2004|pp=243–252}}
* {{Harvnb|Crevier|1993|pp=52–107}}
* {{Harvnb|Moravec|1988|p=9}}
* {{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 of the 1960s, research in the U.S. was heavily funded by the [[DARPA|Department of Defense]]<ref>
[[DARPA]] pours money into undirected pure research into AI during the 1960s:
* {{Harvnb|McCorduck|2004|pp=131}}
* {{Harvnb|Crevier|1993|pp=51, 64-65}}
* {{Harvnb|NRC|1999|pp=204–205}}
</ref>
and laboratories had been established around the world.<ref>
AI in England:
* {{Harvnb|Howe|1994}}
</ref>
AI's founders were profoundly optimistic about the future of the new field: [[Herbert Simon]] predicted that "machines 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>
and [[Marvin Minsky]] agreed, writing that "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>

They had failed to recognize the difficulty of some of the problems they faced.<ref>
See {{See section|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. The next few years, when funding for projects was hard to find, would later be called an "[[AI winter]]".<ref>
First [[AI Winter]], [[Mansfield Amendment]], [[Lighthill report]]
* {{Harvnb|Crevier|1993|pp=115–117}}
* {{Harvnb|Russell|Norvig|2003|p=22}}
* {{Harvnb|NRC|1999|pp=212–213}}
* {{Harvnb|Howe|1994}}
</ref>

In the early 1980s, AI research was revived by the commercial success of [[expert systems]],<ref name=EXPERT>
Expert systems:
* {{Harvnb|ACM|1998|loc=I.2.1|ref=ACM1998}},
* {{Harvnb|Russell|Norvig|2003|pp=22−24}}
* {{Harvnb|Luger|Stubblefield|2004|pp=227–331}},
* {{Harvnb|Nilsson|1998|loc=chpt. 17.4}}
* {{Harvnb|McCorduck|2004|pp=327–335, 434-435}}
* {{Harvnb|Crevier|1993|pp=145–62, 197−203}}
</ref>
a form of AI program that simulated the knowledge and analytical skills of one or more human experts. By 1985 the market for AI had reached over a billion dollars. At the same time, Japan's [[fifth generation computer]] project inspired the U.S and British governments to restore funding for academic research in the field.<ref>
Boom of the 1980s: rise of [[expert systems]], [[Fifth generation computer|Fifth Generation Project]], [[Alvey]], [[Microelectronics and Computer Technology Corporation|MCC]], [[Strategic Computing Initiative|SCI]]:
* {{Harvnb|McCorduck|2004|pp=426–441}}
* {{Harvnb|Crevier|1993|pp=161–162,197-203, 211, 240}}
* {{Harvnb|Russell|Norvig|2003|p=24}}
* {{Harvnb|NRC|1999|pp=210–211}}
</ref>
However, beginning with the collapse of the [[Lisp Machine]] market in 1987, AI once again fell into disrepute, and a second, longer lasting [[AI winter]] began.<ref>
Second [[AI Winter]]:
* {{Harvnb|McCorduck|2004|pp=430–435}}
* {{Harvnb|Crevier|1993|pp=209–210}}
* {{Harvnb|NRC|1999|pp=214–216}}
</ref>

In the 1990s and early 21st century, AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence is used for [[logistics]], [[data mining]], [[medical diagnosis]] and many other areas throughout the technology industry.<ref name="AI_APPS"/>
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 name=VOTN>
Formal methods are now preferred ("Victory of the [[neats vs. scruffies|neats]]"):
* {{Harvnb|Russell|Norvig|2003|pp=25–26}}
* {{Harvnb|McCorduck|2004|pp=486–487}}
</ref>

==Problems ==<!--- This is linked to in the introduction to the article and to the "AI research" section -->
The general problem of simulating (or creating) intelligence has been broken down into a number of specific sub-problems. These consist of particular traits or capabilities that researchers would like an intelligent system to display. The traits described below have received the most attention.<ref name="I" />

===Deduction, reasoning, problem solving ===<!-- This is linked to in the introduction -->
<!-- SOLVED PROBLEMS -->
Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans were often assumed to 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|loc=chpt. 2,3,7,9}},
* {{Harvnb|Luger|Stubblefield|2004|loc=chpt. 3,4,6,8}},
* {{Harvnb|Nilsson|1998|loc=chpt. 7-12}}
</ref>
By the late 1980s 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>

<!-- TWO IMPORTANT UNSOLVED PROBLEMS -->
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>
[[Intractability (complexity)|Intractability]] and efficiency and the [[combinatorial explosion]]:
* {{Harvnb|Russell|Norvig|2003|pp=9, 21-22}}
</ref>

Human beings solve most of their problems using fast, intuitive judgments rather than the conscious, step-by-step deduction that early AI research was able to model.<ref>
[[Cognitive science]] has provided several famous examples:
* {{Harvtxt|Wason|Shapiro|1966}} showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive [[social intelligence]], performance dramatically improves. (See [[Wason selection task]])
* {{Harvtxt|Kahnemann|Slovik|Tversky|1982}} have shown that people are terrible at elementary problems that involve uncertain reasoning. (See [[list of cognitive biases]] for several examples).
* {{Harvtxt|Lakoff|Núñez|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> AI has made some progress at imitating this kind of "sub-symbolic" problem solving: [[embodied agent]] approaches emphasize the importance of [[sensorimotor]] skills to higher reasoning; [[neural net]] research attempts to simulate the structures inside human and animal brains that give rise to this skill.

===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|ref=ACM1998}},
* {{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 all birds. [[John McCarthy (computer scientist)|John McCarthy]] identified this problem in 1969<ref>
{{Harvnb|McCarthy|Hayes|1969}}. While McCarthy was primarily concerned with issues in the logical representation of actions, {{Harvnb|Russell|Norvig|2003}} apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge.</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 (logic)|circumscription]], [[closed world assumption]], [[abductive reasoning|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>

;The breadth of [[commonsense 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]] (e.g., [[Cyc]]) require enormous amounts of laborious [[ontology engineering|ontological engineering]] — they must be built, by hand, one complicated concept at a time.<ref>
Breadth of commonsense knowledge:
* {{Harvnb|Russell|Norvig|2003|p=21}},
* {{Harvnb|Crevier|1993|pp=113–114}},
* {{Harvnb|Moravec|1988|p=13}},
* {{Harvnb|Lenat|Guha|1989}} (Introduction)
</ref> A major goal is to have the computer understand enough concepts to be able to learn by reading from sources like the internet, and thus be able to add to its own ontology.

;The subsymbolic form of some [[commonsense knowledge]]: Much of what people know is not represented as "facts" or "statements" that they could actually say out loud. For example, a chess master will avoid a particular chess position because it "feels too exposed"<ref>{{Harvnb|Dreyfus|Dreyfus|1986}}</ref> or an art critic can take one look at a statue and instantly realize that it is a fake.<ref>{{Harvnb|Gladwell|2005}}</ref> These are intuitions or tendencies that are represented in the brain non-consciously and sub-symbolically.<ref name="Intuition">
Expert knowledge as [[embodied agent|embodied]] intuition:
* {{Harvnb|Dreyfus|Dreyfus|1986}} ([[Hubert Dreyfus]] is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See [[Dreyfus' critique of AI]])
* {{Harvnb|Gladwell|2005}} (Gladwell's ''[[Blink (book)|Blink]]'' is a popular introduction to sub-symbolic reasoning and knowledge.)
* {{Harvnb|Hawkins|Blakeslee|2005}} (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.)
</ref> Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that [[situated artificial intelligence|situated AI]] or [[computational intelligence]] will provide ways to represent this kind of knowledge.<ref name="Intuition"/>

===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|ref=ACM1998}},
* {{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) and be able to make choices that maximize the [[utility]] (or "value") of the available choices.<ref name = "IVT"/>

In classical 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]] uses the [[cooperation]] and [[competition]] of many agents to achieve a given goal. [[Emergent behavior]] such as this is used by [[evolutionary algorithms]] 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}}

[[Machine learning]]<ref>
[[machine learning|Learning]]:
* {{Harvnb|ACM|1998|loc=I.2.6|ref=ACM1998}},
* {{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>
has been central to AI research from the beginning.<ref>
[[Alan Turing]] discussed the centrality of learning as early as 1950, in his classic paper [[Computing Machinery and Intelligence]]. {{Harv|Turing|1950}}
</ref>
[[Unsupervised learning]] is the ability to find patterns in a stream of input. [[Supervised learning]] includes both [[statistical classification|classification]] and numerical [[regression analysis|regression]]. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression takes a set of numerical input/output examples and attempts to discover a continuous function that would generate the outputs from the inputs. In [[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 of [[decision theory]], using concepts like [[utility (economics)|utility]]. The mathematical analysis of machine learning algorithms and their performance is a branch of [[theoretical computer science]] known as [[computational learning theory]].

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

[[Natural language processing]]<ref>
[[Natural language processing]]:
* {{Harvnb|ACM|1998|loc=I.2.7|ref=ACM1998}}
* {{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 that humans 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 -->
{{Main|Robotics}}

The field of [[robotics]]<ref>
[[Robotic]]s:
* {{Harvnb|ACM|1998|loc=I.2.9|ref=ACM1998}},
* {{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|Norvig|2003|pp=916–932}}
</ref>
and [[motion planning|navigation]], with sub-problems of [[Robot localization|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|2003|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|ref=ACM1998}}
* {{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|ref=ACM1998}}
*{{Harvnb|Russell|Norvig|2003|pp=568–578}}
</ref>
[[facial recognition system|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}}
[[File:Kismet robot at MIT Museum.jpg|thumb|200px|[[Kismet (robot)|Kismet]], a robot with rudimentary social skills]]

Emotion and social skills<ref>
Emotion and [[affective computing]]:
* {{Harvnb|Minsky|2007}}
* {{Harvnb|Picard|1997}}
</ref>
play two roles for an intelligent agent. First, 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.) Also, 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 have normal emotions itself.

===Creativity===<!-- This is linked to in the introduction -->
{{Main|Computational creativity}}
[[File:TOPIO 3.0.jpg|right|200px|thumbnail|[[TOPIO]], a robot that can play [[table tennis]], developed by [[TOSY]].]]

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). A related area of computational research is [[Artificial Intuition]] and [[Artificial Imagination]].

===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.<ref>[[Gerald Edelman]], [[Igor Aleksander]] and others have both argued that [[artificial consciousness]] is required for strong AI. {{Harv|Aleksander|1995}} {{Harv|Edelman|2007}} [[Ray Kurzweil]], [[Jeff Hawkins]] and others have argued that strong AI requires a simulation of the operation of the human brain. {{Harv|Hawkins|Blakeslee|2004}} {{Harv|Kurzweil|2005}}</ref>

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 is being talked 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>
[[AI complete]]: {{Harvnb|Shapiro|1992|p=9}}
</ref>

==Approaches==
There is no established unifying theory or [[paradigm]] that guides AI research. Researchers disagree about many issues.<ref>
[[Nils Nilsson]] writes: "Simply put, there is wide disagreement in the field about what AI is all about." {{Harv|Nilsson|1983|p=10}}
</ref>
A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence, by studying [[psychology]] or [[neurology]]? Or is human biology as irrelevant to AI research as bird biology is to [[aeronautical engineering]]?<ref name=BIOLOGY>
Biological intelligence vs. intelligence in general:
* {{Harvnb|Russell|Norvig|2003|pp=2–3}}, who make the analogy with [[aeronautical engineering]].
* {{Harvnb|McCorduck|2004|pp=100–101}}, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplioshed, and the other aimed at modeling intelligent processes found in nature, particularly human ones."
* {{Harvnb|Kolata|1982}}, a paper in ''[[Science (journal)|Science]]'', which describes [[John McCarthy (computer scientist)|McCathy's]] indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real"[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 ]. McCarthy recently reiterated his position at the [[AI@50]] conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence" {{Harv|Maker|2006}}.
</ref>
Can intelligent behavior be described using simple, elegant principles (such as [[logic]] or [[optimization (mathematics)|optimization]])? Or does it necessarily require solving a large number of completely unrelated problems?<ref name=NEATSCRUFFY>
[[Neats vs. scruffies]]:
* {{Harvnb|McCorduck|2004|pp=421–424, 486-489}}
* {{Harvnb|Crevier|1993|pp=168}}
* {{Harvnb|Nilsson|1983|pp=10–11}}
</ref>
Can intelligence be reproduced using high-level symbols, similar to words and ideas? Or does it require "sub-symbolic" processing?<ref name=SUBSYMBOLIC>
Symbolic vs. sub-symbolic AI:
* {{Harvtxt|Nilsson|1998|p=7}}, who uses the term "sub-symbolic".
</ref>

===Cybernetics and brain simulation===
{{Main|Cybernetics|Computational neuroscience}}
[[File:ArtificialFictionBrain.png|thumb|right|280px|There is no consensus on how closely the brain should be [[computer simulation|simulated]].]]
In the 1940s and 1950s, 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 University]] and the [[Ratio Club]] in England.<ref name="IMMEDIATE PRECURSORS"/> By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.

===Symbolic===
{{Main|Good old fashioned artificial intelligence}}
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 [[Allen 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 used the results of  [[psychology|psychological]] experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at [[Carnegie Mellon University]] would eventually culminate in the development of the [[Soar (cognitive architecture)|Soar]] architecture in the middle 80s.<ref>
Cognitive simulation, [[Allen Newell|Newell]] and [[Herbert Simon|Simon]], AI at [[Carnegie Mellon University|CMU]] (then called [[Carnegie Tech]]):
* {{Harvnb|McCorduck|2004|pp=139–179, 245-250, 322-323 (EPAM)}}
* {{Harvnb|Crevier|1993|pp=145–149}}
</ref><ref>
[[Soar (cognitive architecture)|Soar]] (history):
* {{Harvnb|McCorduck|2004|pp=450–451}}
* {{Harvnb|Crevier|1993|pp=258–263}}
</ref>

; Logic based: 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 name=BIOLOGY/> 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]].<ref>
[[John McCarthy (computer scientist)|McCarthy]] and AI research at [[Stanford Artificial Intelligence Laboratory|SAIL]] and [[Stanford Research Institute|SRI]]:
* {{Harvnb|McCorduck|2004|pp=251–259}}
* {{Harvnb|Crevier|1993}}<!-- Page number needed -->
</ref> Logic was also focus of the work at the [[University of Edinburgh]] and elsewhere in Europe which led to the development of the programming language [[Prolog]] and the science of [[logic programming]].<ref>
AI research at [[University of Edinburgh|Edinburgh]] and in [[France]], birth of [[Prolog]]:
* {{Harvnb|Crevier|1993|pp=193–196}}
* {{Harvnb|Howe|1994}}
</ref>

; "Anti-logic" or "scruffy": Researchers at [[MIT]] (such as [[Marvin Minsky]] and [[Seymour Papert]])<ref>
[[AI]] at [[MIT]] under [[Marvin Minsky]] in the 1960s :
* {{Harvnb|McCorduck|2004|pp=259–305}}
* {{Harvnb|Crevier|1993|pp=83–102, 163-176}}
* {{Harvnb|Russell|Norvig|2003|p=19}}
</ref> found that solving difficult problems in [[computer vision|vision]] and [[natural language processing]] required ad-hoc solutions &ndash; they argued that there was 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 [[Carnegie Mellon University|CMU]] and [[Stanford]]).<ref name=NEATSCRUFFY/> [[Commonsense knowledge bases]] (such as [[Doug Lenat]]'s [[Cyc]]) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.<ref>
[[Cyc]]:
* {{Harvnb|McCorduck|2004|p=489}}, who calls it "a determinedly scruffy enterprise"
* {{Harvnb|Crevier|1993|pp=239−243}}
* {{Harvnb|Russell|Norvig|2003|p=363−365}}
* {{Harvnb|Lenat|Guha|1989}}
</ref>

; Knowledge based: When computers with large memories became available around 1970, researchers from all three traditions began to build [[knowledge representation|knowledge]] into AI applications.<ref>
Knowledge revolution:
* {{Harvnb|McCorduck|2004|pp=266–276, 298-300, 314, 421}}
* {{Harvnb|Russell|Norvig|2003|pp=22–23}}
</ref> 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 name=EXPERT/> The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

===Sub-symbolic===
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]].
</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 name=SUBSYMBOLIC/>

; Bottom-up, [[embodied agent|embodied]], [[situated]], [[behavior-based AI|behavior-based]] or [[nouvelle AI]]: Researchers from the related field of [[robotics]], such as [[Rodney Brooks]], rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.<ref>
[[Embodied agent|Embodied]] approaches to AI:
* {{Harvnb|McCorduck|2004|pp=454–462}}
* {{Harvnb|Brooks|1990}}
* {{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. This coincided with the development of the [[embodied mind thesis]] in the related field of [[cognitive science]]: the idea that aspects of the body (such as  movement, perception and visualization) are required for higher intelligence.

; Computational Intelligence:Interest in [[neural networks]] and "[[connectionism]]" was revived by [[David Rumelhart]] and others in the middle 1980s.<ref>
Revival of [[connectionism]]:
* {{Harvnb|Crevier|1993|pp=214–215}}
* {{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>

===Statistical===
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]]). [[Stuart J. Russell|Stuart Russell]] and [[Peter Norvig]] describe this movement as nothing less than a "revolution" and "the victory of the [[neats and scruffies|neats]]."<ref name="VOTN"/>

===Integrating the approaches===
;Intelligent agent paradigm: An [[intelligent agent]] is a system that perceives its 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 humans.<ref name="IA">
The [[intelligent agent]] paradigm:
* {{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. 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>

;[[Agent architecture]]s and [[cognitive architecture]]s: Researchers have designed systems to build intelligent systems out of interacting [[intelligent agents]] in a [[multi-agent system]].<ref>
[[Agent architecture]]s, [[hybrid intelligent system]]s:
* {{Harvtxt|Russell|Norvig|2003|pp=27, 932, 970-972}}
* {{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==
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 and optimization===
{{Main|Search algorithm|Optimization (mathematics)|Evolutionary computation}}

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 [[Logical consequence|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, a process called [[means-ends analysis]].<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 use search algorithms based on [[optimization (mathematics)|optimization]].

Simple exhaustive searches<ref>
Uninformed 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>
are rarely sufficient for most real world problems: the [[search algorithm|search space]] (the number of places to search) quickly grows to [[astronomical]] numbers. The result is a search that is [[Computation time|too slow]] or never completes. The solution, for many problems, is to use "[[heuristics]]" or "rules of thumb" that eliminate choices that are unlikely to lead to the goal (called "[[pruning (algorithm)|pruning]] the [[search tree]]"). [[Heuristics]] supply the program with a "best guess" for what path the solution lies on.<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>

A very different kind of search came to prominence in the 1990s, based on the mathematical theory of [[optimization (mathematics)|optimization]]. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind [[hill climbing]]: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are [[simulated annealing]], [[beam search]] and [[random optimization]].<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>

[[Evolutionary computation]] uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, [[natural selection|selecting]] only the fittest to survive each generation (refining the guesses). Forms of [[evolutionary computation]] include [[swarm intelligence]] algorithms (such as [[ant colony optimization|ant colony]] or [[particle swarm optimization]])<ref>
[[Artificial life]] and society based learning:
* {{Harvnb|Luger|Stubblefield|2004|pp=530–541}}
</ref>
and [[evolutionary algorithms]] (such as [[genetic algorithms]]<ref>
[[Genetic algorithm]]s for learning:
* {{Harvnb|Luger|Stubblefield|2004|pp=509–530}},
* {{Harvnb|Nilsson|1998|loc=chpt. 4.2}}.
See also:
{{cite book |last=Holland |first=John H. |year=1975 |title=Adaptation in Natural and Artificial Systems | publisher=University of Michigan Press | isbn = 0262581116}}
</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 |isbn=0262111705}}
</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>).

===Logic===
{{Main|Logic programming|Automated reasoning}}

[[Logic]]<ref>
[[Logic]]:
* {{Harvnb|ACM|1998|loc=~I.2.3|ref=ACM1998}},
* {{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.<ref name="HLP">
History of logic programming:
* {{Harvnb|Crevier|1993|pp=190–196}}.
* {{Harvnb|Howe|1994}}
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>

Several different forms of logic are used in AI research. [[Propositional logic|Propositional]] or [[sentential 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>
is the logic of statements which can be true or false. [[First-order logic]]<ref>
[[First-order logic]] and features such as [[equality (mathematics)|equality]]:
* {{Harvnb|ACM|1998|loc=~I.2.4|ref=ACM1998}},
* {{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 (mathematical logic)|predicate]]s, and can express facts about objects, their properties, and their relations with each other. [[Fuzzy logic]],<ref name="FL">
[[Fuzzy logic]]:
* {{Harvnb|Russell|Norvig|2003|pp=526–527}}
</ref>
is 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. [[Subjective logic]] models uncertainty in a different and more explicit manner than fuzzy-logic: a given binomial opinion satisfies belief + disbelief + uncertainty = 1 within a [[Beta distribution]]. By this method, ignorance can be distinguished from probabilistic statements that an agent makes with high confidence.
[[Default logic]]s, [[non-monotonic logic]]s and [[circumscription (logic)|circumscription]]<ref name="NML" /> are forms of logic designed to help with default reasoning and the [[qualification problem]]. 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}}

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>
[[Judea Pearl]]'s contribution to AI:
* {{Harvnb|Russell|Norvig|2003|pp=25–26}}
</ref><ref>
Stochastic methods for uncertain reasoning:
* {{Harvnb|ACM|1998|loc=~I.2.3|ref=ACM1998}},
* {{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 a 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 theory|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>
Stochastic temporal models:
{{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>
or [[Kalman filter]]s<ref>
[[Kalman filter]]:
* {{Harvnb|Russell|Norvig|2003|pp=551–557}}
</ref>).

A key concept from the science of [[economic]]s is "[[utility]]": a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using [[decision theory]], [[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>
These tools include models such as [[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]] 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.<ref>
Statistical learning methods and [[classifier (mathematics)|classifiers]]:
* {{Harvnb|Russell|Norvig|2003|pp=712–754}},
* {{Harvnb|Luger|Stubblefield|2004|pp=453–541}}
</ref>

A classifier can be trained in various ways; there are many statistical and [[machine learning]] approaches. 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 learning|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 tasks. 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 in search and optimization|no free lunch]]" theorem. Determining a suitable classifier for a given problem is still more an art than science.<ref>
{{Harvnb|van der Walt|Bernard |2006}}
</ref>

===Neural networks===
{{Main|Neural networks|Connectionism}}
[[File: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 in the decade before the field AI research was founded, in the work of [[Walter Pitts]] and [[Warren McCullough]]. Other important early researchers were [[Frank Rosenblatt]], who invented the [[perceptron]] and [[Paul Werbos]] who developed the [[backpropagation]] algorithm.<ref>
[[Backpropagation]]:
* {{Harvnb|Russell|Norvig|2003|pp=744–748}},
* {{Harvnb|Luger|Stubblefield|2004|pp=467–474}},
* {{Harvnb|Nilsson|1998|loc=chpt. 3.3}}
</ref>

The main categories of networks are acyclic or [[feedforward neural network]]s (where the signal passes in only one direction) and [[recurrent neural network]]s (which allow feedback). Among the most popular feedforward networks are [[perceptrons]], [[multi-layer perceptron]]s and [[radial basis network]]s.<ref>
[[Feedforward network]]s, [[perceptron]]s [[radial basis network]]s:
* {{Harvnb|Russell|Norvig|2003|pp=739–748, 758}}
* {{Harvnb|Luger|Stubblefield|2004|pp=458–467}}
</ref>
Among recurrent networks, the most famous is the [[Hopfield net]], a form of attractor network, which was first described by [[John Hopfield]] in 1982.<ref name="RecNet">
[[Recurrent networks]], [[Hopfield nets]]:
* {{Harvnb|Russell|Norvig|2003|p=758}}
* {{Harvnb|Luger|Stubblefield|2004|pp=474–505}}
</ref>
Neural networks can be applied to the problem of [[intelligent control]] (for robotics) or [[machine learning|learning]], using such techniques as [[Hebbian learning]] and [[competitive 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>

[[Jeff Hawkins]] argues that research in neural networks has stalled because it has failed to model the essential properties of the [[neocortex]], and has suggested a model ([[Hierarchical Temporal Memory]]) that is loosely based on neurological research.<ref>
{{Harvnb|Hawkins|Blakeslee|2004}}
</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|ref=ACM1998}},
* {{Harvnb|Russell|Norvig|2003|pp=926–932}}
</ref>

===Languages===
{{Main|List of programming languages for artificial intelligence}}

<!-- Not sure where to put this in the article. I think it deserves no more weight than this single sentence. To be improved. -->
AI researchers have developed several specialized languages for AI research, including [[Lisp programming language|Lisp]]<ref>
[[Lisp (programming language)|Lisp]]:
* {{Harvnb|Luger|Stubblefield|2004|pp=723–821}}
* {{Harvnb|Crevier|1993|pp=59–62}},
* {{Harvnb|Russell|Norvig|2003|p=18}}
</ref> and [[Prolog]].<ref>
[[Prolog]]:
* {{Harvnb|Poole|Mackworth|Goebel|1998|pp=477–491}},
* {{Harvnb|Luger|Stubblefield|2004|pp=641–676, 575-581}}
</ref>

==Evaluating progress==
{{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 [[draughts]] is optimal,<ref>
{{cite journal | url=http://www.sciencemag.org/cgi/content/abstract/1144079 | title=Checkers Is Solved | date=2007-07-19 | accessdate=2007-07-20 | journal=Science | first=Jonathan | last=Schaeffer| volume=317 | issue=5844 | pages=1518–1522 | doi=10.1126/science.1144079 | pmid=17641166 }}
</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.

A quite different approach measures machine intelligence through tests which are developed from ''mathematical'' definitions of intelligence. Examples of these kinds of tests start in the late nineties devising intelligence tests using notions from [[Kolmogorov Complexity]] and [[data compression]].<ref>{{cite journal | title = Beyond the Turing Test | journal = Journal of Logic, Language and Information | author = Jose Hernandez-Orallo | url = http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.44.8943 |  accessdate = 2009-07-21 | year = 2000 | pages = 447–466 | volume = 9  | issue = 4 | doi = 10.1023/A:1008367325700 | citeseerx = 10.1.1.44.8943 }}</ref>
<ref>{{cite journal | title = A computational extension to the Turing Test | journal = Proceedings of the 4th Conference of the Australasian Cognitive Science Society | author =  D L Dowe and A R Hajek | url = http://www.csse.monash.edu.au/publications/1997/tr-cs97-322-abs.html |  accessdate = 2009-07-21 | year = 1997 }}</ref>  Similar definitions of machine intelligence have been put forward by Marcus Hutter in his book ''Universal Artificial Intelligence'' (Springer 2005), an idea further developed by Legg and Hutter.<ref>{{cite journal | title = Universal Intelligence: A Definition of Machine Intelligence | journal = Minds and Machines | author = Shane Legg and Marcus Hutter | url = http://www.vetta.org/documents/UniversalIntelligence.pdf | accessdate = 2009-07-21 | year = 2007 | pages = 391–444 | volume = 17 | issue = 4 | doi = 10.1007/s11023-007-9079-x | arxiv = 0712.3329 }}</ref>  Two major advantages of mathematical definitions are their applicability to nonhuman intelligences and their absence of a requirement for human testers.

==Applications==
{{Expand section|date=July 2009}}
{{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, [[game artificial intelligence|video games]], toys, and [[Web search engines]].  Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence, sometimes described as the [[AI effect]].<ref>
{{cite news
  | 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]].

===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 behavior, data-mining, driverless cars, robot soccer and games.

===Platforms===
A [[platform (computing)|platform]] (or "[[computing platform]]")is defined as "some sort of hardware architecture or software framework (including application frameworks), that allows software to run." As Rodney Brooks <ref>Brooks, R.A., "How to build complete creatures rather than isolated cognitive simulators," in K. VanLehn (ed.), Architectures for Intelligence, pp. 225-239, Lawrence Erlbaum Assosiates, Hillsdale, NJ, 1991.</ref> pointed out many years ago, it is not just the artificial intelligence software that defines the AI features of the platform, but rather the actual platform itself that affects the AI that results, i.e., we need to be working out AI problems on real world platforms rather than in isolation.

A wide variety of platforms has allowed different aspects of AI to develop, ranging from [[expert systems]], albeit PC-based but still an entire real-world system to various robot platforms such as the widely available Roomba with open interface.<ref>http://hackingroomba.com/?s=atmel</ref>

==Philosophy==
{{Main|Philosophy of artificial intelligence}}

Artificial intelligence, by claiming to be able to recreate the capabilities of the human [[mind]], is both a challenge and an inspiration for [[philosophy]]. Are there limits to how intelligent machines can be? Is there an essential difference between human intelligence and artificial intelligence? Can a machine have a [[mind]] and [[consciousness]]? A few of the most influential answers to these questions are given below.<ref>
All of these positions below are mentioned in standard discussions of the subject, such as:
* {{Harvnb|Russell|Norvig|2003|pp=947–960}}
* {{Harvnb|Fearn|2007|pp=38–55}}
</ref>

;[[Computing Machinery and Intelligence|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 a machine based on its behavior. This theory forms the basis of the [[Turing test]].<ref>
Philosophical implications of the [[Turing test]]:
* {{Harvnb|Turing|1950}}
* {{Harvnb|Haugeland|1985|pp=6–9}}
* {{Harvnb|Crevier|1993|p=24}}
* {{Harvnb|McCorduck|2004|p=70–71}}
* {{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 name=DP>
[[Dartmouth Conferences|Dartmouth proposal]]:
* {{Harvnb|McCarthy|Minsky|Rochester|Shannon|1955}}
* {{Harvnb|Crevier|1993|p=49}}
</ref>
; [[Physical symbol system|Newell and Simon's physical symbol system hypothesis]]: ''"A physical symbol system has the necessary and sufficient means of general intelligent action."'' Newell and Simon argue that intelligences consists of formal operations on symbols.<ref name=NS>
The [[physical symbol system]]s hypothesis:
* {{Harvnb|Newell|Simon|1976|p=116}}
* {{Harvnb|McCorduck|2004|p=153}}
* {{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. (See [[Dreyfus' critique of AI]].)<ref>
Dreyfus criticized the [[necessary and sufficient|necessary]] condition of the [[physical symbol system]] hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules". {{Harv|Dreyfus|1992|p=156}}</ref><ref>
[[Dreyfus' critique of artificial intelligence]]:
* {{Harvnb|Dreyfus|1972}}, {{Harvnb|Dreyfus|Dreyfus|1986}}
* {{Harvnb|Crevier|1993|pp=120–132}}
* {{Harvnb|McCorduck|2004|p=211–239}}
* {{Harvnb|Russell|Norvig|2003|pp=950–952}},
</ref>
; [[Gödel's incompleteness theorem]]: ''A [[formal system]] (such as a computer program) can not prove all true statements.'' [[Roger Penrose]] is among those who claim that Gödel's theorem limits what machines can do. (See ''[[The Emperor's New Mind]]''.)<ref>
This is a paraphrase of the important implication of Gödel's theorems.</ref><ref>
The Mathematical Objection:
* {{Harvnb|Russell|Norvig|2003|p=949}}
* {{Harvnb|McCorduck|2004|p=448–449}}
Making the Mathematical Objection:
* {{Harvnb|Lucas|1961}},
* {{Harvnb|Penrose|1989}}.
Refuting Mathematical Objection:
* {{Harvnb|Turing|1950}} under “(2) The Mathematical Objection”
* {{Harvnb|Hofstadter|1979}},
Background:
* {{Harvnb|Ref=none|Gödel|1931}}, {{Harvnb|Ref=none|Church|1936}}, {{Harvnb|Ref=none|Kleene|1935}}, {{Harvnb|Ref=none|Turing|1937}}
</ref>
; [[Chinese room#Strong AI|Searle's strong AI hypothesis]]: ''"The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."''<ref>
This version is from {{Harvtxt|Searle|1999}}, and is also quoted in {{Harvnb|Dennett|1991|p=435}}. Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states."  {{Harv|Searle|1980|p=1}}. Strong AI is defined similarly by {{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."</ref> 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>
Searle's [[Chinese Room]] argument:
* {{Harvnb|Searle|1980}}, {{Harvnb|Searle|1991}}
* {{Harvnb|Russell|Norvig|2003|pp=958–960}}
* {{Harvnb|McCorduck|2004|pp=443–445}}
* {{Harvnb|Crevier|1993|pp=269–271}}
</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.<ref>
[[Artificial brain]]:
* {{Harvnb|Moravec|1988}}
* {{Harvnb|Kurzweil|2005|p=262}}
* {{Harvnb|Russell|Norvig|2003|p=957}}
* {{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. Daniel Dennett sees human consciousness as multiple functional thought patterns; see "Consciousness Explained". {{Harvnb|Dennett|1991}}
</ref>

==Prediction==
{{Main|Artificial intelligence in fiction|Ethics of artificial intelligence|Transhumanism|Technological singularity}}

AI is a common topic in both [[science fiction]] and projections about the future of technology and society. The existence of an artificial intelligence that rivals human intelligence raises difficult ethical issues, and the potential power of the technology inspires both hopes and fears.

<!-- IMPACT ON SOCIETY: FICTION -->
In fiction, AI has appeared fulfilling many roles, including
a servant ([[R2D2]] in ''[[Star Wars]]''),
a law enforcer ([[K.I.T.T.]] "[[Knight Rider (1982 TV series)|Knight Rider]]"),
a comrade ([[Data (Star Trek)|Lt. Commander Data]] in ''[[Star Trek: The Next Generation]]''),
a conqueror/overlord (''[[The Matrix]]''),
a dictator (''[[With Folded Hands]]''),
an assassin (''[[Terminator (series)|Terminator]]''),
a sentient race (''[[Battlestar Galactica (re-imagining)|Battlestar Galactica]]''/[[Transformers]]),
an extension to human abilities (''[[Ghost in the Shell]]'')
and the savior of the human race ([[R. Daneel Olivaw]] in the ''[[Foundation Series]]'').

<!--ROBOT RIGHTS / THE ABILITY TO SUFFER, i.e. SENTIENCE -->
[[Mary Shelley]]'s ''[[Frankenstein]]''<ref>
{{Harvtxt|McCorduck|2004|p=190-25}} discusses ''[[Frankenstein]]'' and identifies the key ethical issues as scientific hubris and the suffering of the monster, i.e. [[robot rights]].
</ref> considers a key issue in the [[ethics of artificial intelligence]]: if a machine can be created that has intelligence, could it also ''[[sentience|feel]]''?  If it can feel, does it have the same rights as a human? The idea also appears in modern [[science fiction]], including the films ''[[Blade Runner]]'' and ''[[A.I.: Artificial Intelligence]]'', in which humanoid machines have the ability to feel human emotions. This issue, now known as "[[robot rights]]", is currently being considered by, for example, California's [[Institute for the Future]],<ref>
[[Robot rights]]:
* {{Harvnb|Russell|Norvig|2003|p=964}}
* [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>

<!-- IMPACT ON SOCIETY: ACADEMIA -->
The impact of AI on society is a serious area of study for [[futurist]]s. Academic sources have considered such consequences as a decreased demand for human labor,<ref>
{{Harvtxt|Russell|Norvig|2003|p=960-961}}
</ref>
the enhancement of human ability or experience,<ref name=TS>
[[The Singularity is Near|Singularity]], [[transhumanism]]:
* {{Harvnb|Kurzweil|2005}}
* {{Harvnb|Russell|Norvig|2003|p=963}}
</ref>
and a need for redefinition of human identity and basic values.<ref>
[[Joseph Weizenbaum]]'s critique of AI:
* {{Harvnb|Weizenbaum|1976}}
* {{Harvnb|Crevier|1993|pp=132−144}}
* {{Harvnb|McCorduck|2004|pp=356–373}}
* {{Harvnb|Russell|Norvig|2003|p=961}}
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.
</ref>
Andrew Kennedy, in his musing on the evolution of the human personality,<ref>
Kennedy, Andrew (2009), 'Who is human anyway?', pp=221–234, "Essential Personalities, and why humans found love, adapted to monogamy and became better parents", Gravity Publishing, UK, {{ISBN|978-0-9544831-4-2}}
</ref>
considered that artificial intelligences or 'new minds' are likely to have severe personality disorders, and identifies four particular types that are likely to arise: the autistic, the collector, the ecstatic, and the victim.  He suggests that they will need humans because of our superior understanding of personality and the role of the unconscious.

<!-- PROJECTIONS / SINGULARITY -->
Several [[futurist]]s argue that artificial intelligence will transcend the limits of progress. [[Ray Kurzweil]] has used [[Moore's law]] (which describes the relentless exponential improvement in digital technology with uncanny accuracy) to calculate that [[desktop computer]]s will have the same processing power as human brains by the year 2029. He also predicts 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/>

<!-- TRANSHUMANISM -->
Robot designer [[Hans Moravec]], cyberneticist [[Kevin Warwick]] and inventor [[Ray Kurzweil]] have predicted that humans and machines will merge in the future into [[cyborg]]s that are more capable and powerful than either.<ref name=TS/>  This idea, called [[transhumanism]], which has roots in [[Aldous Huxley]] and [[Robert Ettinger]], has been illustrated in fiction as well, for example in the [[manga]] ''[[Ghost in the Shell]]'' and the science-fiction series ''[[Dune (novel)|Dune]]''.

<!-- AI AS EVOLUTION -->
[[Edward Fredkin]] argues that "artificial intelligence is the next stage in evolution,"<ref>
Quoted in {{Harvtxt|McCorduck|2004|p=401}}
</ref> 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 in 1998.

[[Pamela McCorduck]] writes that all these scenarios are expressions of the ancient human desire to, as she calls it, "forge the gods".<ref name="MCCORDUCK"/>

==See also==
{{Portal box|AI|Mind and Brain}}

{{Main|Outline of artificial intelligence}}

* [[Artificial Intelligence (journal)]]
* [[List of scientific journals]]
* [[List of notable artificial intelligence projects|List of AI projects]]
* [[:Category:Artificial intelligence researchers|List of AI researchers]]
* [[List of emerging technologies]]
* [[List of basic artificial intelligence topics]]
* [[List of important publications in computer science#Artificial intelligence|List of important AI publications]]
* [[Technological singularity]]
*[[Philosophy of mind]]
*[[Cognitive sciences]]
*[[Artificial intelligence in fiction]]
<!-- 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|colwidth=30em}}

==References==
===Major AI textbooks===
{{refbegin}}
* {{cite book | first1=George | last1=Luger | author-link=George Luger
| first2=William | last2=Stubblefield | author2-link=William Stubblefield
| year=2004
| title=Artificial Intelligence: Structures and Strategies for Complex Problem Solving|edition=5th
| publisher=The Benjamin/Cummings Publishing Company, Inc.
| isbn=0-8053-4780-1 | url=http://www.cs.unm.edu/~luger/ai-final/tocfull.html}}
* {{cite book | 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}}
* {{cite book | first1 = David | last1 = Poole | author-link=David Poole (researcher)| first2 = Alan | last2 = Mackworth | author2-link=Alan Mackworth
| first3 = Randy | last3 = Goebel | author3-link=Randy Goebel
| publisher = Oxford University Press | place = New York | year = 1998
| title = Computational Intelligence: A Logical Approach | url = http://www.cs.ubc.ca/spider/poole/ci.html }}
* {{cite book | first = Patrick Henry | last = Winston | author-link = Patrick Winston
| publisher = Addison-Wesley | year = 1984 | place = Reading, Massachusetts | isbn = 0201082594
| title = Artificial Intelligence}}
{{refend}}

===History of AI===
{{refbegin}}
* {{Crevier 1993}}
* {{McCorduck 2004}}
{{refend}}

===Other sources===
{{refbegin}}
* {{cite web |ref={{harvid|ACM|1998}}
| publisher=[[Association of Computing Machinery|ACM]]
| year=1998
| title=ACM Computing Classification System: Artificial intelligence
| url=http://www.acm.org/class/1998/I.2.html | accessdate=2007-08-30
}}
* {{Cite book
| last = Aleksander | first= Igor | authorlink = Igor Aleksander
| year=1995
| title= Artificial Neuroconsciousness: An Update
| publisher=IWANN
| url = http://www.ee.ic.ac.uk/research/neural/publications/iwann.html
| archiveurl = http://web.archive.org/web/19970302014628/http://www.ee.ic.ac.uk/research/neural/publications/iwann.html
| archivedate = 1997-03-02
}} [http://dblp.uni-trier.de/rec/bibtex/conf/iwann/Aleksander95 BibTex] [http://web.archive.org/web/19970302014628/http://www.ee.ic.ac.uk/research/neural/publications/iwann.html Internet Archive]
* {{Cite journal | last=Brooks | first=Rodney | authorlink=Rodney Brooks
| year =1990
| title = Elephants Don't Play Chess
| journal = Robotics and Autonomous Systems | volume=6 | issue=1–2 | pages=3–15
| url=http://people.csail.mit.edu/brooks/papers/elephants.pdf | accessdate=2007-08-30
| doi=10.1016/S0921-8890(05)80025-9
}}.
* {{Cite magazine | last=Buchanan | first = Bruce G.
| title = A (Very) Brief History of Artificial Intelligence
| magazine = AI Magazine | date = Winter 2005 | pages=53–60
| url=http://www.aaai.org/AITopics/assets/PDF/AIMag26-04-016.pdf | accessdate=2007-08-30
}}
* {{cite book | last=Dennett | first=Daniel | author-link=Daniel Dennett
| year=1991
| title=[[Consciousness Explained]]
| publisher=The Penguin Press
| isbn= 0-7139-9037-6
}}
* {{cite book | last=Dreyfus | first=Hubert | authorlink = Hubert Dreyfus
| year = 1972
| title = [[What Computers Can't Do]]
| publisher = MIT Press | location = New York
| isbn = 0060110821
}}
* {{cite book | last=Dreyfus | first=Hubert | authorlink = Hubert Dreyfus
| year =1979
| title = What Computers ''Still'' Can't Do
| publisher = MIT Press | location = New York
| isbn=0262041340
}}
* {{cite book | last1=Dreyfus | first1=Hubert  | authorlink = Hubert Dreyfus
| last2 = Dreyfus | first2 = Stuart
| year = 1986
| title = Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer
| publisher = Blackwell | location = Oxford, UK
| isbn=0029080606
}}
* {{cite book | last=Dreyfus | first=Hubert | authorlink = Hubert Dreyfus
| year =1992
| title = What Computers ''Still'' Can't Do
| publisher = MIT Press | location = New York
| isbn=0-262-54067-3
}}
* {{cite web | last = Edelman | first = Gerald | authorlink = Gerald Edelman
| date = 23 November 2007
| title = Gerald Edelman - Neural Darwinism and Brain-based Devices
| url = http://lis.epfl.ch/resources/podcast/2007/11/gerald-edelman-neural-darwinism-and.html
| publisher = Talking Robots
}}
* {{cite book | last=Fearn
| first = Nicholas
| year =2007
| title= The Latest Answers to the Oldest Questions: A Philosophical Adventure with the World's Greatest Thinkers
| publisher = Grove Press
| location=New York
}}
* {{cite web | last = Forster | first =  Dion | authorlink= Dion Forster
 | year = 2006
 | title = Self validating consciousness in strong artificial intelligence: An African theological contribution.
 | url = http://www.spirituality.org.za/files/D%20Forster%20doctorate.pdf
 | publisher = University of South Africa | location = Pretoria
}}
* {{cite book | last = Gladwell | first =  Malcolm | authorlink= Malcolm Gladwell
 | year = 2005
 | title = [[Blink (book)|Blink]]
 | isbn = 0-316-17232-4
 | publisher = Little, Brown and Co. | location = New York
}}
* {{cite book | 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
}}
* {{cite book | last1=Hawkins | first1=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
}}
* {{cite book | last=Hofstadter | first = Douglas | author-link = Douglas Hofstadter
| year = 1979
| title = [[Gödel, Escher, Bach|Gödel, Escher, Bach: an Eternal Golden Braid]]
| isbn=0394745027
| publisher=Vintage Books
| location=New York, NY
}}
* {{cite web | first = J. | last = Howe
| date = November 1994
| title = Artificial Intelligence at Edinburgh University: a Perspective
| url=http://www.inf.ed.ac.uk/about/AIhistory.html | retrieval-date= 30 August 2007
}}.
* {{cite book | last1=Kahneman | first1=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
| publisher=Cambridge University Press | location=New York
| isbn=0521284147
}}
* {{Cite journal | first = G. | last=Kolata
| year=1982
| title=How can computers get common sense?
| journal=Science | issue=4566 | pages=1237–1238
| doi = 10.1126/science.217.4566.1237
| volume = 217
 | pmid = 17837639 }}
* {{cite book | last=Kurzweil | first=Ray | author-link=Ray Kurzweil
| year=1999
| title=[[The Age of Spiritual Machines]]
| publisher=Penguin Books
| isbn=0-670-88217-8
}}
* {{cite book | last=Kurzweil | first=Ray | author-link=Ray Kurzweil
| year=2005
| title=[[The Singularity is Near]]
| publisher=Penguin Books
| isbn=0-670-03384-7
}}
* {{cite book | first = George | last = Lakoff |author-link=George Lakoff
| year = 1987
| title = Women, Fire, and Dangerous Things: What Categories Reveal About the Mind
| publisher = University of Chicago Press
| isbn= 0-226-46804-6
}}
* {{cite book | last1=Lakoff | first1=George | author-link=George Lakoff
| last2=Núñez | first2=Rafael E. | author2-link=Rafael E. 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
}}.
* {{cite book | last1=Lenat | first1=Douglas | author-link=Douglas Lenat
| last2=Guha | first2=R. V.
| year = 1989
| title = Building Large Knowledge-Based Systems
| publisher = Addison-Wesley
| isbn=0201517523
}}
* {{Cite journal | last=Lighthill | first = Professor Sir James | author-link=James Lighthill
| year = 1973
| contribution= Artificial Intelligence: A General Survey
| title = Artificial Intelligence: a paper symposium
| publisher = Science Research Council
}}
* {{cite book | last=Lucas | first= John | author-link = John Lucas (philosopher)
| year = 1961
| contribution=Minds, Machines and Gödel
| editor-last = Anderson | editor-first =A.R.
| title=Minds and Machines
| url = http://users.ox.ac.uk/~jrlucas/Godel/mmg.html | retrieval-date= 30 August 2007
}}
* {{cite web | last = Maker | first = Meg Houston
| year = 2006
| title = AI@50: AI Past, Present, Future | location=Dartmouth College
| url = http://www.engagingexperience.com/2006/07/ai50_ai_past_pr.html | accessdate=16 October 2008
}}
* {{cite web | last1 = McCarthy | first1 = John | authorlink1 = John McCarthy (computer scientist)
| last2 = Minsky | first2 = Marvin | authorlink2 = Marvin Minsky
| last3 = Rochester | first3 = Nathan | authorlink3 = Nathan Rochester
| last4 = Shannon | first4 = Claude | authorlink4 = Claude Shannon
| year = 1955
| title = A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence
| url = http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html | retrieval-date= 30 August 2007
}}.
* {{Cite journal | last1 = McCarthy | first1 = John | author-link = John McCarthy (computer scientist)
| last2 = Hayes | first2=P. J.
| year = 1969
| title= Some philosophical problems from the standpoint of artificial intelligence
| journal =Machine Intelligence | volume= 4 | pages = 463–502
| url=http://www-formal.stanford.edu/jmc/mcchay69.html | retrieval-date= 30 August 2007
}}
* {{cite book | last=Minsky | first=Marvin | author-link=Marvin Minsky
| year = 1967
| title = Computation: Finite and Infinite Machines
| publisher = Prentice-Hall | location=Englewood Cliffs, N.J.
| isbn=0131654497
}}
* {{cite book | 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
}}
* {{cite web | last=Moravec | first=Hans | author-link=Hans Moravec
| year = 1976
| title = The Role of Raw Power in Intelligence
| url= http://www.frc.ri.cmu.edu/users/hpm/project.archive/general.articles/1975/Raw.Power.html  | retrieval-date= 30 August 2007
}}
* {{cite book | last=Moravec | first=Hans | author-link=Hans Moravec
| year = 1988
| title = Mind Children
| publisher = Harvard University Press
| isbn=0674576160
}}
* {{cite book | last=NRC | first=(United States National Research Council) | author-link=United States National Research Council
| year=1999
| chapter=Developments in Artificial Intelligence
| title=Funding a Revolution: Government Support for Computing Research
| publisher=National Academy Press
}}
* {{cite book | last=Needham | first=Joseph | authorlink = Joseph Needham
| year=1986
| title=[[Science and Civilization in China|Science and Civilization in China: Volume 2]]
| publisher=Caves Books Ltd.
}}
* {{cite book | last1 = Newell | first1 = Allen  | author-link=Allen Newell
| last2 = Simon | first2=H. A. | author-link2=Herbert Simon
| year = 1963
| contribution=GPS: A Program that Simulates Human Thought
| editor-last= Feigenbaum | editor-first= E.A.
| editor2-last= Feldman | editor2-first= J.
| title=Computers and Thought
| publisher= McGraw-Hill | publication-place= New York
}}
* {{Cite journal| last1 = Newell | first1 = Allen  | author-link=Allen Newell
| last2 = Simon | first2=H. A. | author-link2=Herbert Simon
| year = 1976
| contribution = Computer Science as Empirical Inquiry: Symbols and Search
| title = Communications of the ACM | volume= 19 | number=3
| url = http://www.rci.rutgers.edu/~cfs/472_html/AI_SEARCH/PSS/PSSH4.html
}}.
* {{Citation<!-- using Citation template (rather than "cite magazine") to avoid the final period -->
| last = Nilsson | first = Nils | author-link = Nils Nilsson
| year = 1983
| title = Artificial Intelligence Prepares for 2001
| journal = AI Magazine | volume = 1 | number = 1
| url = http://ai.stanford.edu/~nilsson/OnlinePubs-Nils/General%20Essays/AIMag04-04-002.pdf
}}, Presidential Address to the [[Association for the Advancement of Artificial Intelligence]].
* {{Cite journal | last = Searle | first = John | author-link=John Searle
| year = 1980
| title = Minds, Brains and Programs
| journal = Behavioral and Brain Sciences | volume = 3 | issue = 3 | pages= 417–457
| doi = 10.1017/S0140525X00005756 | url = http://www.bbsonline.org/documents/a/00/00/04/84/bbs00000484-00/bbs.searle2.html
}}
* {{cite book | last=Searle | first=John | author-link=John Searle
| year = 1999
| title = Mind, language and society
| publisher = Basic Books | location = New York, NY
| isbn = 0465045219 | oclc = 231867665 43689264
}}
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| last2 = Detlor | first2 = Brian
| year = 2004
| title = Intelligent agents as innovations
| journal = AI and Society | volume = 18 | issue = 4 | pages= 364–381
| doi = 10.1007/s00146-004-0310-5 | url =http://foba.lakeheadu.ca/serenko/papers/Serenko_Detlor_AI_and_Society.pdf
}}
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| last3 = Cocosila | first3 = Mihail
| year = 2007
| title = Unplanned effects of intelligent agents on Internet use: Social Informatics approach
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}}
* {{cite book | last=Shapiro | first= Stuart C. | editor-first=Stuart C. | editor-last=Shapiro
| year=1992
| contribution=Artificial Intelligence
| title=Encyclopedia of Artificial Intelligence | edition=2nd | pages=54–57
| url=http://www.cse.buffalo.edu/~shapiro/Papers/ai.pdf
| publisher= John Wiley | location=New York
| isbn=0471503061
}}
* {{cite book | last=Simon | first = H. A. | author-link=Herbert Simon
| year = 1965
| title=The Shape of Automation for Men and Management
| publisher = Harper & Row | publication-place = New York
}}
* {{Turing 1950}}.
* {{cite web| last1=van der Walt | first1=Christiaan  |  last2=Bernard | first2 =Etienne
| year=2006<!--year is presumed based on acknowledgements at the end of the article-->
| title= Data characteristics that determine classifier performance
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}}
* {{cite book | last1=Wason | first1=P. C. | author-link=Peter Cathcart Wason
| last2=Shapiro | first2=D.
| editor=Foss, B. M.
| year=1966
| title=New horizons in psychology
| location=Harmondsworth | publisher=Penguin
| chapter=Reasoning
}}
* {{cite book | last=Weizenbaum | first = Joseph | authorlink=Joseph Weizenbaum
| year = 1976
| title = [[Computer Power and Human Reason]]
| publisher = W.H. Freeman & Company | location = San Francisco
| isbn = 0716704641
}}
{{refend}}

==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
* John Johnston, (2008) "The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI", MIT Press
* Courtney Boyd Myers ed. (2009). [http://www.forbes.com/2009/06/22/singularity-robots-computers-opinions-contributors-artificial-intelligence-09_land.html The AI Report]. Forbes June 2009

==External links==
{{Sisterlinks|Artificial Intelligence}}
* [http://www-formal.stanford.edu/jmc/whatisai/whatisai.html What Is AI?] — An introduction to artificial intelligence by AI founder [[John McCarthy (computer scientist)|John McCarthy]].
* {{SEP|logic-ai|Logic and Artificial Intelligence|Richmond Thomason}}
* [http://www.gpai.cc GPAI] - An open project for everyone come together to develop AI under [[GPL]] and [[Creative Commons]].
* [http://www.aaai.org/AITopics/ AI Topics] — A large directory of links and other resources maintained by the [[Association for the Advancement of Artificial Intelligence]], the leading organization of academic AI researchers.
* [https://www.researchgate.net/group/Artificial_Intelligence Artificial Intelligence Discussion group]

{{Clear}}<!-- cmd to lower template out of another template's space -->
{{philosophy of science}}
{{philosophy of mind}}

{{DEFAULTSORT:Artificial Intelligence}} <!--
[[Category:Artificial intelligence]]
[[Category:Cybernetics]]
[[Category:Formal sciences]]
[[Category:Technology in society]]-->

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[[bg:Изкуствен интелект]]
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[[hu:Mesterséges intelligencia]]
[[ml:കൃത്രിമബുദ്ധി]]
[[mr:कृत्रिम बुद्धिमत्ता]]
[[arz:ذكاء صناعى]]
[[ms:Kecerdasan buatan]]
[[mn:Хиймэл оюун ухаан]]
[[nl:Kunstmatige intelligentie]]
[[new:आर्टिफिसियल इन��टेलिजेन्स]]
[[ja:人工知能]]
[[no:Kunstig intelligens]]
[[nn:Kunstig intelligens]]
[[pl:Sztuczna inteligencja]]
[[pt:Inteligência artificial]]
[[ksh:Artificial Intelligence]]
[[ro:Inteligenţă artificială]]
[[ru:Искусственный интеллект]]
[[sah:Оҥоһуу интеллект]]
[[simple:Artificial intelligence]]
[[sk:Umelá inteligencia]]
[[sl:Umetna inteligenca]]
[[sr:Вјештачка интелигенција]]
[[sh:Umjetna inteligencija]]
[[fi:Tekoäly]]
[[sv:Artificiell intelligens]]
[[tl:Intelehensiyang Artipisyal]]
[[ta:செயற்கை அறிவாண்மை]]
[[te:కృత్రిమ మేధస్సు]]
[[th:ปัญญาประดิษฐ์]]
[[tr:Yapay zekâ]]
[[tk:Ýasama akyl]]
[[uk:Штучний інтелект]]
[[ur:مصنوعی ذہانت]]
[[vec:Inteligensa artificial]]
[[vi:Trí tuệ nhân tạo]]
[[war:Artipisyal nga intelihensya]]
[[zh-yue:人工智能]]
[[bat-smg:Dėrbtėns intelekts]]
[[zh:人工智能]]
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