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：图表中最短路径问题是AI理论和应用的基石。 现有的算法通常忽略边重计算时间。 在本文中,我们提出了一个加权定向图表的通用框架,可以多次计算(估计)边重,提高准确度和运行时间成本。 这就提出了一个普遍最短路径问题,可以优化路径成本及其不确定性的不同方面。 我们为普遍问题提出了一个完整的随时解决方案算法,并用经验证明了其有效性。</s>