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A decidable first-order logic for knowledge representation

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Abstract

Decidable first-order logics with reasonable model-theoretic semantics have several benefits for knowledge representation. These logics have the expressive power of standard first order logic along with an inference algorithm that will always terminate, both important considerations for knowledge representation. Knowledge representation systems that include a faithful implementation of one of these logics can also use its model-theoretic semantics to provide meanings for the data they store. One such logic, a variant of a simple type of first-order relevance logic, is developed and its properties described. This logic, although extremely weak, does capture a non-trivial and well-motivated set of inferences that can be entrusted to a knowledge representation system.

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Authors and Affiliations

  1. AT&T Bell Laboratories, Artificial Intelligence Principles Research Department, 600 Mountain Avenue, 07974, Murray Hill, New Jersey, USA

    Peter F. Patel-Schneider

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  1. Peter F. Patel-Schneider
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This is a revised and much extended version of a paper of the same name that appears in the Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, California, 1985.

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Patel-Schneider, P.F. A decidable first-order logic for knowledge representation. J Autom Reasoning 6, 361–388 (1990). https://doi.org/10.1007/BF00244354

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  • DOI: https://doi.org/10.1007/BF00244354

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