188 research outputs found
Value Compression of Pattern Databases
One common pattern database compression technique is to merge adjacent database entries and store the minimum of merged entries to maintain heuristic admissibility. In this paper we propose a compression technique that preserves every entry, but reduces the number of bits used to store each entry, therefore limiting the values that can be represented. Even when this technique throws away low values in the heuristic, it can still have better performance than the traditional approach. We develop a theoretical basis for selecting which values to keep and show improved performance in both unidirectional and bidirectional search
Tightest Admissible Shortest Path
The shortest path problem in graphs is fundamental to AI. Nearly all variants
of the problem and relevant algorithms that solve them ignore edge-weight
computation time and its common relation to weight uncertainty. This implies
that taking these factors into consideration can potentially lead to a
performance boost in relevant applications. Recently, a generalized framework
for weighted directed graphs was suggested, where edge-weight can be computed
(estimated) multiple times, at increasing accuracy and run-time expense. We
build on this framework to introduce the problem of finding the tightest
admissible shortest path (TASP); a path with the tightest suboptimality bound
on the optimal cost. This is a generalization of the shortest path problem to
bounded uncertainty, where edge-weight uncertainty can be traded for
computational cost. We present a complete algorithm for solving TASP, with
guarantees on solution quality. Empirical evaluation supports the effectiveness
of this approach.Comment: arXiv admin note: text overlap with arXiv:2208.1148
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