S. Koenig, M. Likhachev and X. Sun. Speeding up Moving-Target Search. In International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2007.

Abstract: In this paper, we study moving-target search, where an agent (= hunter) has to catch a moving target (= prey). The agent does not necessarily know the terrain initially but can observe it within a certain sensor range around itself. It uses the strategy to always move on a shortest presumed unblocked path toward the target, which is a reasonable strategy for computer-controlled characters in video games. We study how the agent can find such paths faster by exploiting the fact that it performs A* searches repeatedly. To this end, we extend Adaptive A*, an incremental heuristic search method, to moving-target search and demonstrate experimentally that the resulting MT-Adaptive A* is faster than isolated A* searches and, in many situations, also D* Lite, a state-of-the-art incremental heuristic search method. In particular, it is faster than D* Lite by about one order of magnitude for moving-target search in known and initially unknown mazes if both search methods use the same informed heuristics. Errata: This version of the paper correct one omission and one mistake. For Lazy MT-Adaptive A*, the user-supplied initial h-values do not only need to be consistent but also satisfy the additional triangle inequality H(s,s'') ≤ H(s,s') + H(s',s'') for all states s, s' and s'', that is also required by D* Lite. Also, Lines 46-47 in the pseudocode of Lazy MT-Adaptive A* were indented too much.

The proofs that Eager and Lazy MT-Adaptive A* use the same h-values can be found in a technical report.

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