Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Artificial Intelligence
Efficient memory-bounded search methods
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
In search of the best constraint satisfaction search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Fast planning through planning graph analysis
Artificial Intelligence
Using regression-match graphs to control search in planning
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
Unifying SAT-based and Graph-based Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on 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
Extending Planning Graphs to an ADL Subset
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Understanding and Extending Graphplan
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Planning as Heuristic Search: New Results
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Exploiting memory in the search for high quality plans on the planning graph
Exploiting memory in the search for high quality plans on the planning graph
Planning graph as a (dynamic) CSP: exploiting EBL, DDB and other CSP search techniques in Graphplan
Journal of Artificial Intelligence Research
AltAltp: online parallelization of plans with heuristic state search
Journal of Artificial Intelligence Research
Accelerating partial-order planners: some techniques for effective search control and pruning
Journal of Artificial Intelligence Research
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
Efficient implementation of the plan graph in STAN
Journal of Artificial Intelligence Research
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
A robust and fast action selection mechanism for planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A Global Filtration for Satisfying Goals in Mutual Exclusion Networks
Recent Advances in Constraints
State agnostic planning graphs and the application to belief-space planning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
State agnostic planning graphs: deterministic, non-deterministic, and probabilistic planning
Artificial Intelligence
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The Graphplan algorithm for generating optimal make-span plans containing parallel sets of actions remains one of the most effective ways to generate such plans. However, despite enhancements on a range of fronts, the approach is currently dominated in terms of speed, by state space planners that employ distance-based heuristics to quickly generate serial plans. We report on a family of strategies that employ available memory to construct a search trace so as to learn from various aspects of Graphplan's iterative search episodes in order to expedite search in subsequent episodes. The planning approaches can be partitioned into two classes according to the type and extent of search experience captured in the trace. The planners using the more aggressive tracing method are able to avoid much of Graphplan's redundant search effort, while planners in the second class trade off this aspect in favor of a much higher degree of freedom than Graphplan in traversing the space of 'states' generated during regression search on the planning graph. The tactic favored by the second approach, exploiting the search trace to transform the depth-first, IDA* nature of Graphplan's search into an iterative state space view, is shown to be the more powerful. We demonstrate that distance-based, state space heuristics can be adapted to informed traversal of the search trace used by the second class of planners and develop an augmentation targeted specifically at planning graph search. Guided by such a heuristic, the step-optimal version of the planner in this class clearly dominates even a highly enhanced version of Graphplan. By adopting beam search on the search trace we then show that virtually optimal parallel plans can be generated at speeds quite competitive with a modern heuristic state space planner.