Proceedings:
Book One
Volume
Issue:
Proceedings of the International Conference on Automated Planning and Scheduling, 29
Track:
Main Track
Downloads:
Abstract:
We describe a new algorithm for generating pattern collections for pattern database heuristics in optimal classical planning. The algorithm uses the counterexample-guided abstraction refinement (CEGAR) principle to guide the pattern selection process. Our experimental evaluation shows that a single run of the CEGAR algorithm can compute informative pattern collections in a fairly short time. Using multiple CEGAR algorithm runs, we can compute much larger pattern collections, still in shorter time than existing approaches, which leads to a planner that outperforms the state-of-the-art pattern selection methods by a significant margin.
DOI:
10.1609/icaps.v29i1.3499
ICAPS
Proceedings of the International Conference on Automated Planning and Scheduling, 29