Symbolic artificial intelligence: Difference between revisions

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is the term for the collection of all methods in artificial intelligence research that are based on high-level [[physical symbol systems hypothesis|symbolic]] (human-readable) representations of problems, [[Formal logic|logic]] and [[search algorithm|search]].<ref>{{Cite journal|last1=Garnelo|first1=Marta|last2=Shanahan|first2=Murray|date=2019-10-01|title=Reconciling deep learning with symbolic artificial intelligence: representing objects and relations|journal=Current Opinion in Behavioral Sciences|language=en|volume=29|pages=17–23|doi=10.1016/j.cobeha.2018.12.010|s2cid=72336067 |doi-access=free|hdl=10044/1/67796|hdl-access=free}}</ref> Symbolic AI used tools such as [[logic programming]], [[production (computer science)|production rules]], [[semantic nets]] and [[frame (artificial intelligence)|frames]], and it developed applications such as [[knowledge-based systems]] (in particular, [[expert systems]]), [[symbolic mathematics]], [[automated theorem provers]], [[ontologies]], the [[semantic web]], and [[automated planning and scheduling]] systems. The Symbolic AI paradigm led to seminal ideas in [[Artificial intelligence#Search and optimization|search]], [[symbolic programming]] languages, [[Intelligent agent|agents]], [[multi-agent systems]], the [[semantic web]], and the strengths and limitations of formal knowledge and [[automated reasoning|reasoning systems]].

Symbolic AI was the dominant [[paradigm]] of AI research from the mid-1950s until the mid-1990s.{{sfn|Kolata|1982}} Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with [[artificial general intelligence]] and considered this the ultimate goal of their field.<ref>{{CitationCite journal |last=Newell |first=Allen |last2=Simon |first2=Herbert A. needed|date=March1976-03-01 2024|title=Computer science as empirical inquiry: symbols and search |url=https://dl.acm.org/doi/10.1145/360018.360022 |journal=Commun. ACM |volume=19 |issue=3 |pages=113–126 |doi=10.1145/360018.360022 |issn=0001-0782}}</ref> An early boom, with early successes such as the [[Logic Theorist]] and [[Arthur Samuel (computer scientist)|Samuel]]'s [[Arthur Samuel (computer scientist)|Checkers Playing Program]], led to unrealistic expectations and promises and was followed by the first [[AI winter|AI Winter]] as funding dried up.{{sfn|Kautz|2022|pp=107-109}}{{sfn|Russell |Norvig|2021|p=19}} A second boom (1969–1986) occurred with the rise of expert systems, their promise of capturing corporate expertise, and an enthusiastic corporate embrace.{{sfn|Russell |Norvig|2021|pp=22-23}}{{sfn|Kautz|2022|pp=109-110}} That boom, and some early successes, e.g., with [[XCON]] at [[Digital Equipment Corporation|DEC]], was followed again by later disappointment.{{sfn|Kautz|2022|pp=109-110}} Problems with difficulties in knowledge acquisition, maintaining large knowledge bases, and brittleness in handling out-of-___domain problems arose. Another, second, AI Winter (1988–2011) followed.{{sfn|Kautz|2022|p=110}} Subsequently, AI researchers focused on addressing underlying problems in handling uncertainty and in knowledge acquisition.{{sfn|Kautz|2022|pp=110-111}} Uncertainty was addressed with formal methods such as [[hidden Markov model]]s, [[Bayesian reasoning]], and [[statistical relational learning]].{{sfn|Russell |Norvig|2021|p=25}}{{sfn|Kautz|2022|p=111}} Symbolic machine learning addressed the knowledge acquisition problem with contributions including [[Version space learning|Version Space]], [[Leslie Valiant|Valiant]]'s [[Probably approximately correct learning|PAC learning]], [[Ross Quinlan|Quinlan]]'s [[ID3 algorithm|ID3]] [[decision-tree]] learning, [[Case-based reasoning|case-based learning]], and [[inductive logic programming]] to learn relations.{{sfn|Kautz|2020|pp=110-111}}

[[Artificial neural network|Neural networks]], a subsymbolic approach, had been pursued from early days and reemerged strongly in 2012. Early examples are [[Frank Rosenblatt|Rosenblatt]]'s [[perceptron]] learning work, the [[backpropagation]] work of Rumelhart, Hinton and Williams,<ref>{{cite journal| doi = 10.1038/323533a0| issn = 1476-4687| volume = 323| issue = 6088| pages = 533–536| last1 = Rumelhart| first1 = David E.| last2 = Hinton| first2 = Geoffrey E.| last3 = Williams| first3 = Ronald J.| title = Learning representations by back-propagating errors| journal = Nature| date = 1986 | bibcode = 1986Natur.323..533R| s2cid = 205001834}}</ref> and work in [[convolutional neural network]]s by LeCun et al. in 1989.<ref>{{Cite journal| volume = 1| issue = 4| pages = 541–551| last1 = LeCun| first1 = Y.| last2 = Boser| first2 = B.| last3 = Denker| first3 = I.| last4 = Henderson| first4 = D.| last5 = Howard| first5 = R.| last6 = Hubbard| first6 = W.| last7 = Tackel| first7 = L.| title = Backpropagation Applied to Handwritten Zip Code Recognition| journal = Neural Computation| date = 1989| doi = 10.1162/neco.1989.1.4.541| s2cid = 41312633}}</ref> However, neural networks were not viewed as successful until about 2012: "Until Big Data became commonplace, the general consensus in the Al community was that the so-called neural-network approach was hopeless. Systems just didn't work that well, compared to other methods. ... A revolution came in 2012, when a number of people, including a team of researchers working with Hinton, worked out a way to use the power of [[GPUs]] to enormously increase the power of neural networks."{{sfn|Marcus |Davis|2019}} Over the next several years, [[deep learning]] had spectacular success in handling vision, [[speech recognition]], speech synthesis, image generation, and machine translation. However, since 2020, as inherent difficulties with bias, explanation, comprehensibility, and robustness became more apparent with deep learning approaches; an increasing number of AI researchers have called for [[Neuro-symbolic AI|combining]] the best of both the symbolic and neural network approaches<ref name="Rossi">