Model Minimization , Regression , andPropositional STRIPS

Propositional STRIPS planning problems can be viewed as nite state automata (FSAs) represented in a factored form. Automaton minimization is a well-known technique for reducing the size of an explicit FSA. Recent work in computer-aided veriication on model checking has extended this technique to provide automaton minimization algorithms for fac-tored FSAs. In this paper, we consider the relationship between STRIPS problem-solving techniques such as regression and the recently developed automaton minimization techniques for factored FSAs. We show that regression computes a partial and approximate minimized form of the FSA corresponding to the STRIPS problem. We then deene a systematic form of regression which computes a partial but exact minimized form of the associated FSA. We also relate minimization to methods for performing reachability analysis to detect irrelevant u-ents. Finally, we show that exact computation of the minimized automaton is NP-complete under the assumption that this automaton is polynomial in size.