Partial Order Reduction for Planning Partial Order Reduction for Planning Abstract of the Thesis Partial Order Reduction for Planning

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 proposed new, stronger POR algorithms based on the new theory. We have also applied these new algorithms Type of Report: MS Thesis Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 WASHINGTON UNIVERSITY IN ST. LOUIS School of Engineering and Applied Science Department of Computer Science and Engineering Thesis Examination Committee: Dr. Jeremy Buhler Dr. Yixin Chen Dr. Chenyang Lu PARTIAL ORDER REDUCTION FOR PLANNING by You Xu A thesis presented to the School of Engineering of Washington University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Dec 2009 Saint Louis, Missouri copyright by You Xu 2009 ABSTRACT OF THE THESISOF 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.