Online Learning of Search Heuristics


Michael Fink ;
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:115-122, 2007.


In this paper we learn heuristic functions that efficiently find the shortest path between two nodes in a graph. We rely on the fact that often, several elementary admissible heuristics might be provided, either by human designers or from formal domain abstractions. These simple heuristics are traditionally composed into a new admissible heuristic by selecting the highest scoring elementary heuristic in each distance evaluation. We suggest that learning a weighted sum over the elementary heuristics can often generate a heuristic with higher dominance than the heuristic defined by the highest score selection. The weights within our composite heuristic are trained in an online manner using nodes to which the true distance has already been revealed during previous search stages. Several experiments demonstrate that the proposed method typically finds the optimal path while significantly reducing the search complexity. Our theoretical analysis describes conditions under which finding the shortest path can be guaranteed.

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