The shortest path problem in graphs is a cornerstone of 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 for the generalized problem, and empirically demonstrate its efficacy.

翻译：图论中的最短路径问题是人工智能理论与应用的基石。现有算法通常忽略边权重的计算时间。本文提出一个面向加权有向图的泛化框架，其中边权重可通过多次计算（估计）获得，且每次计算的精度与运行时间成本递增。由此引出一个泛化最短路径问题，需优化路径成本及其不确定性的不同方面。我们为该泛化问题提出一种完整的任意时刻求解算法，并通过实验验证其有效性。