Stochastic Node Caching for Memory-Bounded Search

Teruhisa Miura, Toru Ishida

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 Nude 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.


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