Editing Symbolic artificial intelligence (section)

====Early work on knowledge representation and reasoning====

Early work covered both applications of formal reasoning emphasizing [[first-order logic]], along with attempts to handle [[Commonsense reasoning|common-sense reasoning]] in a less formal manner.

===== Modeling formal reasoning with logic: the "neats" =====
{{Main|logic programming}}
Unlike Simon and Newell, [[John McCarthy (computer scientist)|John McCarthy]] felt that machines did not need to simulate the exact mechanisms of human thought, but could instead try to find the essence of abstract reasoning and problem-solving with logic,{{sfn|Russell|Norvig|2021|loc=p. 9 (logicist AI), p. 19 (McCarthy's work)}} regardless of whether people used the same algorithms.{{efn|
McCarthy once said: "This is AI, so we don't care if it's psychologically real".{{sfn|Kolata|1982}} McCarthy reiterated his position in 2006 at the [[AI@50]] conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence".{{sfn|Maker|2006}} [[Pamela McCorduck]] writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones.",{{sfn|McCorduck|2004|pp=100–101}} 
[[Stuart J. Russell|Stuart Russell]] and [[Peter Norvig]] wrote "Aeronautical engineering texts do not define the goal of their field as making 'machines that fly so exactly like pigeons that they can fool even other pigeons.'"{{sfn|Russell|Norvig|2021|p=2}}}}
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]], planning and [[machine learning|learning]].{{sfn|McCorduck|2004|pp=251–259}}
Logic was also the 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.{{sfn|Crevier|1993|pp=193–196}}{{sfn|Howe|1994}}

===== Modeling implicit common-sense knowledge with frames and scripts: the "scruffies" =====
{{Main|neats vs. scruffies}}

Researchers at [[MIT]] (such as [[Marvin Minsky]] and [[Seymour Papert]]){{sfn|McCorduck|2004|pp=259–305}}{{sfn|Crevier|1993|pp=83–102, 163–176}}{{sfn|Russell|Norvig|2021|p=19}} found that solving difficult problems in [[computer vision|vision]] and [[natural language processing]] required ad hoc solutions—they argued that no simple and general principle (like [[logic]]) 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).{{sfn|McCorduck|2004|pp=421–424, 486–489}}{{sfn|Crevier|1993|p=168}}
[[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.{{sfn|McCorduck|2004|p=489}}{{sfn|Crevier|1993|pp=239–243}}{{sfn|Russell|Norvig|2021|p=316, 340}}