Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Fast algorithms for finding randomized strategies in game trees
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Rational Coordination in Multi-Agent Environments
Autonomous Agents and Multi-Agent Systems
An efficient heuristic approach for security against multiple adversaries
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
A Heuristic-Based Approach for a Betting Strategy in Texas Hold'em Poker
Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008
Factored models for probabilistic modal logic
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
GIB: steps toward an expert-level bridge-playing program
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Artificial Intelligence
RVAB: rational varied-depth search in Siguo game
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
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Work on game playing in AI has typically ignored games of imperfect information such as poker. In this paper we present a framework for dealing with such games. We point out several important issues that arise only in the context of imperfect information games particularly the insufficiency of a simple game tree model to represent the players information state and the need for randomization in the players optimal strategies. We describe Gala an implemented system that provides the user with a very natural and expressive language for describing games. From a game description Gala creates an augmented game tree with information sets which can be used by various algorithms in order to find optimal strategies for that game. In particular Gala implements the first practical algorithm for finding optimal randomized strategies in two player imperfect information competitive games [Koller et al 1994]. The running time of this algorithm is palinomial in the size of the game tree whereas previous algorithms were exponential. We present experimental results showing that this algorithm is also efficient in practice and can therefore form the basis for a game playing system.