Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems.
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The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.
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Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the middle 1990s.
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Symbolic AI machine learning addressed the knowledge acquisition problem with contributions including Version Space, Valiant's PAC learning, Quinlan's ID3 decision-tree learning, case-based learning, and inductive logic programming to learn relations.
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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 combining the best of both the symbolic and neural network approaches and addressing areas that both approaches have difficulty with, such as common-sense reasoning.
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Symbolic AI approach was succinctly expressed in the "physical symbol systems hypothesis" proposed by Newell and Simon in 1976:.
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Symbolic AI machine learning approaches were investigated to address the knowledge acquisition bottleneck.
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Symbolic AI machine learning was applied to learning concepts, rules, heuristics, and problem-solving.
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Integration of the symbolic and connectionist paradigms of AI has been pursued by a relatively small research community over the last two decades and has yielded several significant results.
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Symbolic AI claimed that two kinds of theories are needed in order to study and model cognition.
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Symbolic AI gave a talk at an AI workshop at Stanford comparing symbols to aether, one of science's greatest mistakes.
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Equally, symbolic AI is not just about production rules written by hand.
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Symbolic AI's approach rejected representations, either symbolic or distributed, as not only unnecessary, but as detrimental.
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