Detail

Combining probability and logic (P-log)

In this talk we will discuss 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 will 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 as well as Pearl’s probabilistic causal models
can be easily mapped to coherent P-log programs.

(Joint CITI / CENTRIA Seminar)