Subset selection of search heuristics

Authors:
Chris Rayner;Nathan Sturtevant;Michael Bowling
Affiliations:
University of Alberta, Edmonton, AB, Canada;University of Denver, Denver, CO;University of Alberta, Edmonton, AB, Canada
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

Citing 5
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Abstract

Constructing a strong heuristic function is a central problem in heuristic search. A common approach is to combine a number of heuristics by maximizing over the values from each. If a limit is placed on this number, then a subset selection problem arises. We treat this as an optimization problem, and proceed by translating a natural loss function into a submodular and monotonic utility function under which greedy selection is guaranteed to be near-optimal. We then extend this approach with a sampling scheme that retains provable optimality. Our empirical results show large improvements over existing methods, and give new insight into building heuristics for directed domains.