Mate with bishop and knight in kriegspiel
Theoretical Computer Science
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
Towards strategic Kriegspiel play with opponent modeling
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Reasoning about partially observed actions
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Representing Kriegspiel states with metapositions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Game-tree search with combinatorially large belief states
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Efficient belief-state AND-OR search, with application to Kriegspiel
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
Anytime algorithms for multi-agent visibility-based pursuit-evasion games
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Priority level planning in kriegspiel
ICEC'12 Proceedings of the 11th international conference on Entertainment Computing
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Partial information games are excellent examples of decision making under uncertainty. In particular, some games have such an immense state space and high degree of uncertainty that traditional algorithms and methods struggle to play them effectively. Monte Carlo tree search (MCTS) has brought significant improvements to the level of computer programs in games such as Go, and it has been used to play partial information games as well. However, there are certain games with particularly large trees and reduced information in which a naive MCTS approach is insufficient: in particular, this is the case of games with long matches, dynamic information, and complex victory conditions. In this paper we explore the application of MCTS to a wargame-like board game, Kriegspiel. We describe and study three MCTS-based methods, starting from a very simple implementation and moving to more refined versions for playing the game with little specific knowledge. We compare these MCTS-based programs to the strongest known minimax-based Kriegspiel program, obtaining significantly better experimental results with less domain-specific knowledge.