Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
The multiple sequence alignment problem in biology
SIAM Journal on Applied Mathematics
Heuristic search in restricted memory (research note)
Artificial Intelligence
Reducing reexpansions in iterative-deepening search by controlling cutoff bounds
Artificial Intelligence
Linear-space best-first search
Artificial Intelligence
Fast recursive formulations for best-first search that allow controlled use of memory
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Controlling the learning process of real-time heuristic search
Artificial Intelligence
Breadth-first heuristic search
Artificial Intelligence
Divide-and-conquer bidirectional search: first results
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Breadth-first heuristic search
Artificial Intelligence
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Linear-space search algorithms such as IDA* (Iterative Deepening A*) cache only those nodes on the current search path, but may revisit the same node again and again. This causes IDA* to take an impractically long time to find a solution. In this paper, we propose a simple and effective algorithm called Stochastic Node Caching (SNC) for reducing the number of revisits. SNC caches a node with the best estimate, which is currently known of the minimum estimated cost from the node to the goal node. Unlike previous related research such as MREC, SNC caches nodes selectively, based on a fixed probability. We demonstrate that SNC can effectively reduce the number of revisits compared to MREC, especially when the state-space forms a lattice.