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@inproceedings{icaps24,
 author = {Eyal Weiss and Ariel Felner and Gal A. Kaminka},
 title = {Tightest Admissible Shortest Path},
 booktitle = ICAPS,
 year = {2024},
  OPTpages= {to appear},
  wwwnote = {also available on arXiv 2308.08453},
  abstract = {  
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.
  },
}