Editing Symbolic artificial intelligence (section)

=== The first AI summer: irrational exuberance, 1948–1966 ===

Success at early attempts in AI occurred in three main areas: artificial neural networks, knowledge representation, and heuristic search, contributing to high expectations. This section summarizes Kautz's reprise of early AI history.

====Approaches inspired by human or animal cognition or behavior====

Cybernetic approaches attempted to replicate the feedback loops between animals and their environments. A robotic turtle, with sensors, motors for driving and steering, and seven vacuum tubes for control, based on a preprogrammed neural net, was built as early as 1948. This work can be seen as an early precursor to later work in neural networks, reinforcement learning, and situated robotics.{{sfn|Kautz|2022|p=106}}

An important early symbolic AI program was the [[Logic theorist]], written by [[Allen Newell]], [[Herbert A. Simon|Herbert Simon]] and [[Cliff Shaw]] in 1955–56, as it was able to prove 38 elementary theorems from Whitehead and Russell's [[Principia Mathematica]]. Newell, Simon, and Shaw later generalized this work to create a domain-independent problem solver, [[General Problem Solver|GPS]] (General Problem Solver). GPS solved problems represented with formal operators via state-space search using [[means-ends analysis]].{{sfn|Newell|Simon|1972}}

During the 1960s, symbolic approaches achieved great success at simulating intelligent behavior in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research was concentrated in four institutions in the 1960s: [[Carnegie Mellon University]], [[Stanford]], [[MIT]] and (later) [[University of Edinburgh]]. Each one developed its own style of research. Earlier approaches based on [[cybernetics]] or [[artificial neural network]]s were abandoned or pushed into the background.

[[Herbert A. Simon|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.{{sfn||McCorduck|2004|pp=139–179, 245–250, 322–323 (EPAM)}}{{sfn|Crevier|1993|pp=145–149}} This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the [[Soar (cognitive architecture)|Soar]] architecture in the middle 1980s.{{sfn|McCorduck|2004|pp=450–451}}{{sfn|Crevier|1993|pp=258–263}}

====Heuristic search====

In addition to the highly specialized domain-specific kinds of knowledge that we will see later used in expert systems, early symbolic AI researchers discovered another more general application of knowledge. These were called heuristics, rules of thumb that guide a search in promising directions: "How can non-enumerative search be practical when the underlying problem is exponentially hard? The approach advocated by Simon and Newell is to employ [[Heuristic (computer science)|heuristics]]: fast algorithms that may fail on some inputs or output suboptimal solutions."{{sfn|Kautz|2022|page=108}} Another important advance was to find a way to apply these heuristics that guarantees a solution will be found, if there is one, not withstanding the occasional fallibility of heuristics: "The [[A* search algorithm|A* algorithm]] provided a general frame for complete and optimal heuristically guided search. A* is used as a subroutine within practically every AI algorithm today but is still no magic bullet; its guarantee of completeness is bought at the cost of worst-case exponential time.{{sfn|Kautz|2022|page=108}}

====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}}