Heuristic guided optimization for propositional planning

Abstract

Planning as Satisfiability is an important approach to Propositional Planning. A serious drawback of the method is its limited scalability, as the instances that arise from large planning problems are often too hard for modern SAT solvers. This work tackles this problem by combining two powerful techniques that aim at decomposing a planning problem into smaller subproblems, so that the satisfiability instances that need to be solved do not grow prohibitively large. The first technique, incremental goal achievement, turns planning into a series of boolean optimization problems, each seeking to maximize the number of goals that are achieved within a limited planning horizon. This is coupled with a second technique, called heuristic guidance, that directs search towards a state which satisfies all goals. Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.