Fast planning through planning graph analysis
Artificial Intelligence
Exploiting symmetry in the planning graph via explanation-guided search
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Extracting Effective and Admissible State Space Heuristics from the Planning Graph
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Planning as Heuristic Search: New Results
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
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We present a major variant of the Graphplan algorithm that employs available memory to transform the depth-first nature of Graphplan's search into an iterative state space view in which heuristics can be used to traverse the search space. When the planner, PEGG, is set to conduct exhaustive search, it produces guaranteed optimal parallel plans 2 to 90 times faster than a version of Graphplan enhanced with CSP speedup methods. By heuristically pruning this search space PEGG produces plans comparable to Graphplan's in makespan, at speeds approaching state-of-the-art heuristic serial planners.