Probabilistic reasoning with answer sets

TLDR

Answer Set Prolog provides the logical foundation and causal Bayes nets provide the probabilistic foundation for the system. The paper introduces P-log, a declarative language that integrates logical and probabilistic reasoning. P-log integrates Answer Set Prolog with causal Bayes nets, and the authors establish sufficiency conditions for program coherency and show how Bayes nets map to coherent P-log programs. The authors present several non‑trivial examples illustrating P-log’s use for knowledge representation and updating, argue that their update approach is more appealing than existing ones, and demonstrate the coherency of P-log programs through sufficiency conditions.

Abstract

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.