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 to solve benchmark problems across various planning domains. Experimental results have shown significant search cost reduction.

Name thesis:degree_name

Master of Arts (MA)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science and Engineering

Year dc:date.available

2009

Author dc:creator

• Xu, You

Contributors dc:contributor

• Yixin Chen

• planning
• space reduction

Language dc:language

English (en)

DOI

https://doi.org/10.7936/K7P26W5D

OAI identifier oai:identifier

oai:openscholarship.wustl.edu:etd-1933