Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
Bayesian A* Tree Search with Expected O(N) Convergence Rates for Road Tracking
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Phase Transitions and Backbones of 3-SAT and Maximum 3-SAT
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
International Journal of Approximate Reasoning
Predicting the performance of IDA* using conditional distributions
Journal of Artificial Intelligence Research
The expected value of hierarchical problem-solving
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
An average-case analysis of branch-and-bound with applications: summary of results
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Depth-first vs. best-first search: new results
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Forward estimation for game-tree search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Flexible and approximate computation through state-space reduction
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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We consider the problem of finding an optimal path leading from the root of a tree to any of its leaves. The tree is known to be uniform, binary, and of height N, and each branch independently may have a cost of 1 or 0 with probability p and 1-p, respectively. We show that for p1/2, every algorithm which guarantees finding an exact cheapest path, or even a path within a fixed cost ratio of the cheapest, must run in exponential average time. If, however, we are willing to accept a near optimal solution almost always, then a pruning algorithm exists which finds such a solution in linear expected time. The algorithm employs a depth-first strategy which stops at regular intervals to appraise its progress and, if the progress does not meet a criterion based on domain-specific knowledge, the current node is irrevocably pruned.