AbstractsComputer Science

The ability to change one's beliefs consistently is essential for sound reasoning in a world where the new information one acquires may invalidate or augment one's current beliefs. Belief revision is the process wherein an agent modifies its beliefs to incorporate the new information received, and knowledge base merging the process wherein the agent is given two or more knowledge bases to merge. We present a binary decision diagram (BDD) - based implementation of Delgrande and Schaub's consistency-based belief change framework. Our system focuses on knowledge base merging with the possible incorporation of integrity constraints, using a BDD solver for consistency checking. We show that the result of merging finite knowledge bases can be represented as a finite formula, and that merging can be streamlined algorithmically by restricting attention to a subset of the vocabulary of the propositional formulas involved. Experimental results and comparisons with related systems are also given.