Abstract

Abstract: Adaptive A* is an incremental version of A* that updates the h-values of the previous A* search to make them more informed and thus future A* searches more focused. In this paper, we show how the A* searches performed by Adaptive A* can reuse part of the path of the previous search and terminate before they expand a goal state, resulting in Path-Adaptive A*. We demonstrate experimentally that Path-Adaptive A* expands fewer states per search and runs faster than Adaptive A* when solving path-planning problems in initially unknown terrain. Errata: In the pseudocode in Figure 4, c(s,a) should be c(s,s') on Line 32 and the statement on Line 46 is unnecessary (but not wrong). Furthermore, this version of the paper already corrects two other small mistakes: The table entries for acyclic and cyclic mazes were swapped and Line 52 in the pseudocode should use s_start instead of s.

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