Monte Carlo tree search in Kriegspiel
Artificial Intelligence
Strategy generation in multi-agent imperfect-information pursuit games
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Tactical operations of multi-robot teams in urban warfare (demonstration)
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
A pursuit-evasion problem-solving strategy based on probability estimation in a planer region
International Journal of Computing Science and Mathematics
| Hi-index | 0.00 |
We investigate algorithms for playing multi-agent visibility-based pursuit-evasion games. A team of pursuers attempts to maintain visibility contact with an evader who actively avoids tracking. We aim for applicability of the algorithms in real-world scenarios; hence, we impose hard constraints on the run-time of the algorithms and we evaluate them in a simulation model based on a real-world urban area. We compare Monte-Carlo tree search (MCTS) and iterative deepening minimax algorithms running on the information-set tree of the imperfect-information game. The experimental results demonstrate that both methods create comparable good strategies for the pursuer, while the later performs better in creating the evader's strategy.