The principal continuation and the killer heuristic
ACM '77 Proceedings of the 1977 annual conference
Heuristic evaluation functions for general game playing
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Fluxplayer: a successful general game player
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Simulation-based approach to general game playing
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
GIB: imperfect information in a computationally challenging game
Journal of Artificial Intelligence Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Strong planning under partial observability
Artificial Intelligence
A skat player based on Monte-Carlo simulation
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
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In this paper we present a full-fledged player for general games with incomplete information specified in the game description language GDL-II. To deal with uncertainty we introduce a method that operates on partial belief states, which correspond to a subset of the set of states building a full belief state. To search for a partial belief state we present depth-first and Monte-Carlo methods. All can be combined with any traditional general game player, e.g., using minimax or UCT search. Our general game player is shown to be effective in a number of benchmarks and the UCT variant compares positively with the one-and-only winner of an incomplete information track at an international general game playing competition.