Applications of parallel processing technologies in heuristic search planning: methodologies and experiments

Abstract

SUMMARYThe goal of this paper is to investigate the application of parallel programming techniques to boost the performance of heuristic search-based planning systems in various aspects. It shows that an appropriate parallelization of a sequential planning system often brings gain in performance and/or scalability. We start by describing general schemes for parallelizing the construction of a plan. We then discuss the applications of these techniques to two domain-independent heuristic search-based planners-a competitive conformant planner (CPA) and a state-of-the-art classical planner (FF). We present experimental results-on both shared memory and distributed memory platforms-which show that the performance improvements and scalability are obtained in both cases. Finally, we discuss the issues that should be taken into consideration when designing a parallel planning system and relate our work to the existing literature.