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Memory-Based Heuristics for Explicit State Spaces

By Nathan R. Sturtevant, Ariel Felner, Max Barrer, Jonathan Schaeffer and Neil Burch


In many scenarios, quickly solving a relatively small search problem with an arbitrary start and arbitrary goal state is important (e.g., GPS navigation). In order to speed this process, we introduce a new class of memorybased heuristics, called true distance heuristics, that store true distances between some pairs of states in the original state space can be used for a heuristic between any pair of states. We provide a number of techniques for using and improving true distance heuristics such that most of the benefits of the all-pairs shortest-path computation can be gained with less than 1 % of the memory. Experimental results on a number of domains show a 6-14 fold improvement in search speed compared to traditional heuristics

Year: 2009
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