Algorithms for Belief State Compression

A knowledge base is an integral part of a logic-based artificial intelligence system. In this paper, we present a variety of algorithms for knowledge base size reduction. Our approach differs from previous approaches in at least three aspects. First, it takes its objects to be support-structured sets of unconstrained, rather than flat sets of syntactically-constrained, logical formulas, which we refer to as belief states. Second, classical notions of minimality and redundancy are replaced by weaker, resource-bounded alternatives based on the support structure. Third, in “lossy” variants of compression, the compressed knowledge base logically implies only a practically-relevant subset of the original knowledge base.