Artificial Intelligence
Moving-Target Search: A Real-Time Search for Changing Goals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeding up moving-target search
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Multiple agents moving target search
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Evaluating strategies for running from the cops
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Generalized Fringe-Retrieving A*: faster moving target search on state lattices
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Cops and robber game without recharging
SWAT'10 Proceedings of the 12th Scandinavian conference on Algorithm Theory
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Moving target search or the game of cops and robbers has been given much attention during the last two decades. It is known that optimal solutions, given a n-cop-win graph, are computable in polynomial time in the size of the input graph. However, a first practical polytime algorithm was only given recently by Hahn et al. [3]. All other known approaches are learning and anytime algorithms that try to approximate the optimal solution. In this work we present four algorithms: an adaptation of Two-Agent IDA*, Proof-Number Search, alpha-beta, and Reverse Minimax A*, a new algorithm. We show how these techniques can be applied to compute optimal moving target search solutions and give benchmarks on their performance for the one cop one robber problem.