Learning Useful Horn Approximations

While the task of answering queries from an arbitrary propositional theory is intractable in general, it can typicallybe performed e ciently if the theory is Horn. This suggests that it may be more e cient to answer queries using a \Horn approximation"; i.e., a horn theory that is semantically similar to the original theory. The utility of any such approximation depends on how often it produces answers to the queries thatthe systemactuallyencounters; we therefore seek an approximation whose expected \coverage" is maximal. Unfortunately, there are several obstacles to achievingthis goal in practice: (i) The optimal approximation depends on the query distribution, which is typicallynotknowna priori;(ii) identifyingthe optimal approximation is intractable, even given the query distribution; and (iii) the optimalapproximation might be too large to guarantee tractable inference. This paper presents an approach that overcomes (or side-steps) each of these obstacles. We de ne a lea...

• Type: Conference
• Year: 1992
• Where: KR
• Authors: Russell Greiner, Dale Schuurmans
• Keywords: Approximation, Automated Reasoning, KR 1992, Optimal Approximation, Query Distribution