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A first-order logic for reasoning under uncertainty using rough sets

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Reasoning with uncertain information is a problem of key importance when dealing with knowledge from real situations. Obtaining the precise numbers required by many uncertainty-handling formalisms can be a problem when building real systems. The theory of rough sets allows us to handle uncertainty without the need for precise numbers, and so has some advantages in such situations. The authors develop a set of symbolic truth values based upon rough sets which may be used to augment predicate logic, and provide methods for combining these truth values so that they may be propagated when augmented logic formulae are used in automated reasoning.

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Authors and Affiliations

  1. Advanced Computation Laboratory, Imperial Cancer Research Fund, PO Box 123, WC2A 3PX, Lincoln's Inn Fields, London, UK

    Simon Parsons

  2. Department of Medical Informatics, Institute of Biomedical Engineering, Graz University of Technology, Brockmangasse 41, A-8010, Graz, Austria

    Miroslav Kubat

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  1. Simon Parsons
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  2. Miroslav Kubat
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Parsons, S., Kubat, M. A first-order logic for reasoning under uncertainty using rough sets. J Intell Manuf 5, 211–223 (1994). https://doi.org/10.1007/BF00123694

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