Combining Logical and Probabilistic Reasoning

Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering

Volume

Papers from the 2006 AAAI Spring Symposium

Contents

This paper describes a family of knowledge representation problems, whose intuitive solutions require reasoning about defaults, the effects of actions, and quantitative probabilities. We describe an extension of the probabilistic logic language P-log, which uses consistency restoring rules to tackle the problems described. We also report the results of a preliminary investigation into the efficiency of our P-log implementation, as compared with ACE, a system developed by Automated Reasoning Group at UCLA.