Partial Order Reduction for Planning

Abstract

OF THE THESIS Partial Order Reduction for Planning by You Xu Master of Science in Computer Science Washington University in St. Louis, 2009 Research Advisor: Professor Yixin Chen Partial Order Reduction (POR) is a technique that reduces the search space by recognizing interchangeable orders between actions and expanding only a subset of all possible orders during the search. It has been extensively studied in model checking and has proven to be an enabling technique for reducing the search space and costs. Several POR algorithms have been proposed in planning, including the Expansion Core (EC) and Stratified Planning (SP) algorithms. Being orthogonal to the development of accurate heuristic functions, these reduction methods show great potential to improve the planning efficiency from a new perspective. However, it is unclear how these POR methods relate to each other and whether there exist stronger reduction methods. In this thesis, we have proposed a unifying theory that provides a necessary and sufficient condition for two actions to be semi-commutative. We have also revealed that semi-commutativity is the central property that enables POR. We have also interpreted both EC and SP algorithms using this new theory. Further, we have ii proposed new, stronger POR algorithms based on the new theory. We have also applied these new algorithms to solve benchmark problems across various planning domains. Experimental results have shown significant search cost reduction.