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Creators/Authors contains: "Yu, Fanhao"

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  1. ; ; (, Proceedings of the International Symposium on Combinatorial Search)
    Finding optimal solutions to state-space search problems often takes too long, even when using A* with a heuristic function. Instead, practitioners often use a tunable approach, such as weighted A*, that allows them to adjust a trade-off between search time and solution cost until the search is sufficiently fast for the intended application. In this paper, we study algorithms for this problem setting, which we call `tunable suboptimal search'. We introduce a simple baseline, called Speed*, that uses distance-to-go information to speed up search. Experimental results on standard search benchmarks suggest that 1) bounded-suboptimal searches suffer overhead due to enforcing a suboptimality bound, 2) beam searches can perform well, but fare poorly in domains with dead-ends, and 3) Speed* provides robust overall performance. 
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