A Generalization of the Shortest Path Problem to Graphs with Multiple Edge-Cost Estimates

The shortest path problem in graphs is a cornerstone for AI theory and applications. Existing algorithms generally ignore edge weight computation time. In this paper we present a generalized framework for weighted directed graphs, where edge weight can be computed (estimated) multiple times, at increasing accuracy and run-time expense. This raises a generalized shortest path problem that optimize different aspects of path cost and its uncertainty. We present a complete anytime solution algorithm, and experiments that empirically demonstrate its efficacy.