Representation generation in an exploratory learning system
Concept formation knowledge and experience in unsupervised learning
Using knowledge about the opponent in game-tree search
Using knowledge about the opponent in game-tree search
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Success in spades: using AI planning techniques to win the world championship of computer bridge
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Using probabilistic knowledge and simulation to play poker
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Probabilistic opponent-model search
Information Sciences: an International Journal - Heuristic Search and Computer Game Playing
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Search and Planning under Incomplete Information: A Study Using Bridge Card Play
Search and Planning under Incomplete Information: A Study Using Bridge Card Play
A Defense Model for Games with Incomplete Information
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
Learning and Exploiting Relative Weaknesses of Opponent Agents
Autonomous Agents and Multi-Agent Systems
GIB: imperfect information in a computationally challenging game
Journal of Artificial Intelligence Research
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Incorporating opponent models into adversary search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning without human expertise: a case study of the double dummy bridge problem
IEEE Transactions on Neural Networks
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Bridge bidding is considered to be one of the most difficult problems for game-playing programs. It involves four agents rather than two, including a cooperative agent. In addition, the partial observability of the game makes it impossible to predict the outcome of each action. In this paper we present a new decision-making algorithm that is capable of overcoming these problems. The algorithm allows models to be used for both opponent agents and partners, while utilizing a novel model-based Monte Carlo sampling method to overcome the problem of hidden information. The paper also presents a learning framework that uses the above decision-making algorithm for co-training of partners. The agents refine their selection strategies during training and continuously exchange their refined strategies. The refinement is based on inductive learning applied to examples accumulated for classes of states with conflicting actions. The algorithm was empirically evaluated on a set of bridge deals. The pair of agents that co-trained significantly improved their bidding performance to a level surpassing that of the current state-of-the-art bidding algorithm.