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Probabilistic reasoning with answer sets

Published online by Cambridge University Press:  01 January 2009

CHITTA BARAL ,
MICHAEL GELFOND  and
NELSON RUSHTON
CHITTA BARAL
Affiliation:
Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287-8809, USA (e-mail: chitta@asu.edu)
MICHAEL GELFOND
Affiliation:
Department of Computer Science, Texas Tech University Lubbock, TX 79409, USA (e-mail: mgelfond@cs.ttu.edu, nrushton@cs.ttu.edu)
NELSON RUSHTON
Affiliation:
Department of Computer Science, Texas Tech University Lubbock, TX 79409, USA (e-mail: mgelfond@cs.ttu.edu, nrushton@cs.ttu.edu)
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Abstract

This paper develops a declarative language, P-log, that combines logical and probabilistic arguments in its reasoning. Answer Set Prolog is used as the logical foundation, while causal Bayes nets serve as a probabilistic foundation. We give several non-trivial examples and illustrate the use of P-log for knowledge representation and updating of knowledge. We argue that our approach to updates is more appealing than existing approaches. We give sufficiency conditions for the coherency of P-log programs and show that Bayes nets can be easily mapped to coherent P-log programs.

Keywords

Logic programminganswer setsprobabilistic reasoningAnswer Set Prolog
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 9 , Issue 1 , January 2009 , pp. 57 - 144
Copyright
Copyright © Cambridge University Press 2009

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