Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Time complexity of iterative-deepening-A
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Sokoban: enhancing general single-agent search methods using domain knowledge
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Complexity results for standard benchmark domains in planning
Artificial Intelligence
Unifying SAT-based and Graph-based Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
The Detection and Exploitation of Symmetry in Planning Problems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A* with Partial Expansion for Large Branching Factor Problems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Breadth-first heuristic search
Artificial Intelligence
Approximation Properties of Planning Benchmarks
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Domain-independent construction of pattern database heuristics for cost-optimal planning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
On the value of good advice: the complexity of A* search with accurate heuristics
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
The deterministic part of IPC-4: an overview
Journal of Artificial Intelligence Research
Understanding planning tasks: domain complexity and heuristic decomposition
Understanding planning tasks: domain complexity and heuristic decomposition
Limits and Possibilities of BDDs in State Space Search
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Completeness and optimality preserving reduction for planning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Completeness-Preserving Pruning for Optimal Planning
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Predicting the performance of IDA* using conditional distributions
Journal of Artificial Intelligence Research
Topological value iteration algorithms
Journal of Artificial Intelligence Research
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Heuristic search under quality and time bounds
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Online speedup learning for optimal planning
Journal of Artificial Intelligence Research
The time complexity of A* with approximate heuristics on multiple-solution search spaces
Journal of Artificial Intelligence Research
Heuristic search when time matters
Journal of Artificial Intelligence Research
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Heuristic search using algorithms such as A* and IDA* is the prevalent method for obtaining optimal sequential solutions for classical planning tasks. Theoretical analyses of these classical search algorithms, such as the well-known results of Pohl, Gaschnig and Pearl, suggest that such heuristic search algorithms can obtain better than exponential scaling behaviour, provided that the heuristics are accurate enough. Here, we show that for a number of common planning benchmark domains, including ones that admit optimal solution in polynomial time, general search algorithms such as A* must necessarily explore an exponential number of search nodes even under the optimistic assumption of almost perfect heuristic estimators, whose heuristic error is bounded by a small additive constant. Our results shed some light on the comparatively bad performance of optimal heuristic search approaches in "simple" planning domains such as GRIPPER. They suggest that in many applications, further improvements in run-time require changes to other parts of the search algorithm than the heuristic estimator.