Cost-Optimal External Planning

Stefan Edelkamp and Shahid Jabbar

This paper considers strategies for external memory based optimal planning. An external breadth-first search exploration algorithm is devised that is guaranteed to find the costoptimal solution. We contribute a procedure for finding the upper bound on the locality of the search in planning graphs that dictates the number of layers that have to be kept to avoid re-openings. We also discuss an external variant of Enforced Hill Climbing. Using relaxed-plan heuristic without helpful-action pruning we have been able to perform large explorations on metric planning problems, providing better plan lengths than have been reported earlier. A novel approach to plan reconstruction in external setting with linear I/O complexity is proposed. We provide external exploration results on some recently proposed planning domains.

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.