Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Heuristic sampling on backtrack trees
Heuristic sampling on backtrack trees
Heuristic sampling: a method for predicting the performance of tree searching programs
SIAM Journal on Computing
Optimal speedup of Las Vegas algorithms
Information Processing Letters
Artificial Intelligence
Time complexity of iterative-deepening-A
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Disjoint pattern database heuristics
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Searching with Pattern Databases
AI '96 Proceedings of the 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Duality in permutation state spaces and the dual search algorithm
Artificial Intelligence
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Limited discrepancy beam search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Predicting the performance of IDA* using conditional distributions
Journal of Artificial Intelligence Research
Linear-space best-first search: summary of results
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Pruning duplicate nodes in depth-first search
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Learning heuristic functions for large state spaces
Artificial Intelligence
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Traditional heuristic search algorithms use the ranking of states that a heuristic function provides to guide the search. In this paper---with the objective of improving suboptimality and runtime of search algorithms when only weak heuristics are available---we present Stratified Tree Search (STS), a suboptimal heuristic search algorithm that uses a heuristic to partition the state space to guide the search. We call this partition a type system. STS assumes that nodes of the same type will lead to solutions of the same cost. Thus, STS expands only one node of each type in every level of search. We show that in general STS offers a good tradeoff between solution quality and search speed by varying the size of the type system. However, in some cases, STS might not provide a fine adjustment of this tradeoff. We present a variant of STS, Beam STS (BSTS), that allows one to make fine adjustments of this tradeoff. BSTS combines the ideas of STS with those of Beam Search. Our empirical results in benchmark domains show that both STS and BSTS can find solutions of lower suboptimality in less time than standard heuristic search algorithms for finding suboptimal solutions.