  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.
