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.


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