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Learning and Reasoning in Symbolic Domains | The Center for Brains, Minds & Machines

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Early AI researchers predicted that symbolic problems - theorem proving and game playing, for example - would be the first for which computers would match human performance. The reasons for this prediction remain compelling: symbolic problems are crisply expressible in formal language, they are largely free of much of the noise and dependence on world knowledge, and human and animal performance on symbolic tasks does not appear to be supported by large swaths of specialized cortex. Unlike problems in areas like sensation and motor control, symbolic problems seem to exist on computers’ “home turf.” It is mysterious, therefore that modern models of symbolic problem solving lag so far behind human performance.

Learning and Reasoning in Symbolic Domains Early AI researchers predicted that symbolic problems - theorem proving and game playing, for example - would be the first for which computers would match human performance. The reasons for this prediction remain compelling: symbolic problems are crisply expressible in formal language, they are largely free of much of the noise and dependence on world knowledge, and human and animal performance on symbolic tasks does not appear to be supported by large swaths of specialized cortex. Unlike problems in areas like sensation and motor control, symbolic pr

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