Sequencing Operator Counts with State-Space Search

Abstract

Heuristic search is the most common approach to solve classical planning tasks optimally. OpSeq is an innovative approach to solve planning tasks that decomposes the planning task into a master problem and a subproblem. The master problem generates an assignment of integer counts for each task operator, and the subproblem verifies if a plan satisfying these counts exists. If a plan does not exist, OpSeq learns a new constraint to inform the master problem. OpSeq solves the subproblem using a SAT solver. In this dissertation, we propose a new solver OpSearch: an A*-based approach to sequence operator counts that uses the frontier of the search to learn a constraint on failure. We show that OpSearch solves the subproblem better than OpSeq. It solves more planning tasks, scales better, and learns constraints more informative than OpSeq. We prove that OpSearch only learns constraints that maintain all optimal solutions. OpSearch extends the research on an entirely new method to solve planning tasks. Also, our solver opens many new lines of research based on decomposition, such as fast and stronger anytime lower bounds, new methods for agile planning, and new approaches to solve diverse planning.