A heuristic search algorithm with modifiable estimate
Artificial Intelligence
Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Reducing reexpansions in iterative-deepening search by controlling cutoff bounds
Artificial Intelligence
Time complexity of iterative-deepening-A
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Adaptive tree search
Journal of the ACM (JACM)
Domain-independent construction of pattern database heuristics for cost-optimal planning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Iterative-deepening-A: an optimal admissible tree search
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
Predicting the performance of IDA* using conditional distributions
Journal of Artificial Intelligence Research
Depth-first vs best-first search
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Predicting the size of IDA*'s search tree
Artificial Intelligence
Predicting the size of depth-first branch and bound search trees
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The memory requirements of best-first graph search algorithms such as A* often prevent them from solving large problems. The best-known approach for coping with this issue is iterative deepening, which performs a series of bounded depth-first searches. Unfortunately, iterative deepening only performs well when successive cost bounds visit a geometrically increasing number of nodes. While it happens to work acceptably for the classic sliding tile puzzle, IDA* fails for many other domains. In this paper, we present an algorithm that adaptively chooses appropriate cost bounds on-line during search. During each iteration, it learns a model of the search tree that helps it to predict the bound to use next. Our search tree model has three main benefits over previous approaches: 1) it will work in domains with real-valued heuristic estimates, 2) it can be trained on-line, and 3) it is able to make predictions with only a small number of training examples. We demonstrate the power of our improved model by using it to control an iterative-deepening A* search on-line. While our technique has more overhead than previous methods for controlling iterative-deepening A*, it can give more robust performance by using its experience to accurately double the amount of search effort between iterations.