Probabilistic reasoning with answer sets

Authors:
Chitta Baral;Michael Gelfond;Nelson Rushton
Affiliations:
Department of computer science and engineering, arizona state university, tempe, az 85287-8809, usa (e-mail: chitta@asu.edu);Department of computer science, texas tech university lubbock, tx 79409, usa (e-mail: mgelfond@cs.ttu.edu, nrushton@cs.ttu.edu);Department of computer science, texas tech university lubbock, tx 79409, usa (e-mail: mgelfond@cs.ttu.edu, nrushton@cs.ttu.edu)
Venue:
Theory and Practice of Logic Programming
Year:
2009

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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.