Probabilistic reasoning with answer sets
Theory and Practice of Logic Programming2009Vol. 9(1), pp. 57–144
Citations Over TimeTop 1% of 2009 papers
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.
Related Papers
- → Weak nonmonotonic probabilistic logics(2005)58 cited
- → Reasoning about the disruption patterns for train system using Bayesian Network and Prolog(2019)3 cited
- → Koyal — A multi-purpose expert system — MD-CoB-CoA knowledge representation using PROLOG in J2SE(2011)1 cited
- → Nonmonotonic Probabilistic Logics between Model-Theoretic Probabilistic Logic and Probabilistic Logic under Coherence(2002)6 cited
- Nonmonotonic Probabilistic Logics between Model-Theoretic Probabilistic Logic and Probabilistic Logic under Coherence(2002)