Reasoning With Characteristic Models

Formal AI systems traditionally represent knowledge using logical formulas. We will show, however, that for certain kinds of information, a modelbased representation is more compact and enables faster reasoning than the corresponding formula-based representation. The central idea behind our work is to represent a large set of models by a subset of characteristic models. More specifically, we examine model-based representations of Horn theories, and show that there are large Horn theories that can be exactly represented by an exponentially smaller set of characteristic models. In addition, we will show that deduction based on a set of characteristic models takes only linear time, thus matching the performance using Horn theories. More surprisingly, abduction can be performed in polynomial time using a set of characteristic models, whereas abduction using Horn theories is NP-complete. This papers appears in the Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93), Washington, D.C., 1993.