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
Optimal solutions for moving target search
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Partial pathfinding using map abstraction and refinement
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Cops and robber game without recharging
SWAT'10 Proceedings of the 12th Scandinavian conference on Algorithm Theory
Hierarchical visibility for guaranteed search in large-scale outdoor terrain
Autonomous Agents and Multi-Agent Systems
Hi-index | 0.00 |
Moving target search (MTS) or the game of cops and robbers has a broad field of application reaching from law enforcement to computer games. Within the recent years research has focused on computing move policies for one or multiple pursuers (cops). The present work motivates to extend this perspective to both sides, thus developing algorithms for the target (robber). We investigate the game with perfect information for both players and propose two new methods, named TrailMax and Dynamic Abstract Trailmax, to compute move policies for the target. Experiments are conducted by simulating games on 20 maps of the commercial computer game Baldur's Gate and measuring survival time and computational complexity. We test seven algorithms: Cover, Dynamic Abstract Minimax, minimax, hill climbing with distance heuristic, a random beacon algorithm, TrailMax and DATrailMax. Analysis shows that our methods outperform all the other algorithms in quality, achieving up to 98% optimality, while meeting modern computer game computation time constraints.