Completeness-Preserving Pruning for Optimal Planning
Coles Amanda, Andrew J. Coles · Frontiers in artificial intelligence and applications · 2010
This paper focuses on efficient methods for pruning the state space in cost-optimal planning. The use of heuristics to guide search and prune irrelevant branches has been widely and successfully explored. However, heuristic computation at every node in the search space is expensive, and even near perfect heuristics still leave a large portion of the search space to be explored [9]. Using up-front analysis to reduce the number of nodes to be considered therefore has great potential. Our contributions are not concerned with heuristic guidance, rather, with orthogonal completeness-preserving pruning techniques that reduce the number of states a planner must explore to find an optimal solution. We present results showing that our techniques can improve upon state-of-the-art optimal planners, both when using blind search and importantly in conjunction with modern heuristics, specifically hLM-CUT[8]. Our techniques are not limited to optimal planning and can also be applied in satisfycing planning.