Combining Logical and Probabilistic Reasoning

Michael Gelfond, Nelson Rushton, Weijun Zhu

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.

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.