Search in games with incomplete information: a case study using Bridge card play
Artificial Intelligence
World-championship-caliber Scrabble
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
GIB: Steps Toward an Expert-Level Bridge-Playing Program
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Searching over metapositions in kriegspiel
CG'04 Proceedings of the 4th international conference on Computers and Games
Overconfidence or paranoia? search in imperfect-information games
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Representing Kriegspiel states with metapositions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Efficient belief-state AND-OR search, with application to Kriegspiel
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Monte Carlo tree search techniques in the game of Kriegspiel
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Monte Carlo tree search in Kriegspiel
Artificial Intelligence
Playing the perfect Kriegspiel endgame
Theoretical Computer Science
Multiple tree for partially observable Monte-Carlo tree search
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Solving kriegspiel endings with brute force: the case of KR vs. k
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Randomized sampling for large zero-sum games
Automatica (Journal of IFAC)
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In games such as kriegspiel chess (a chess variant where players have no direct knowledge of the opponent's pieces' locations) the belief state's sizes dwarf those of other partial information games like bridge, scrabble, and poker-and there is no easy way to generate states satisfying the given observations. We show that statistical sampling approaches can be developed to do well in such games. We show that it is not necessary for the random sample to consist only of game boards that satisfy each and every one of a player's observations. In fact, we win 24% more often by beginning with such completely consistent boards and gradually switching (as the game progressed) to boards that are merely consistent with the latest observation. This surprising result is explained by noting that as the game progresses, a board that is consistent with the last move becomes more and more likely to be consistent with the entire set of observations, even if we have no idea what sequence of moves might have actually generated this board.