THE APPROXIMATION OF IMPLICATES AND EXPLANATIONS

This paper studies the approximation of the set of minimal implicates and the effect this approximation has on their corresponding minimally consistent explanations. A general definition for approximated minima l implicates, called selective implicates, is presented. Three specifi c instances of selective implicates: query-based, ATMS-based and lengt h-based are studied. Using the set of query-based minimal implicates a nd its approximation, explanations are generated and the properties of these explanations are studied. The goal of these studies is to propo se a framework for incorporating knowledge-guided and resource-bounded approximation into computational abduction. The potential benefit mig ht include the discovery of a useful and tractable approximation strat egy for computational abduction.