A Monte-Carlo approach to uncertain inference
Artificial intelligence and its applications
Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Search in games with incomplete information: a case study using Bridge card play
Artificial Intelligence
Finding optimal strategies for imperfect information games
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Learning in multiagent systems
Multiagent systems
Optimal Play against Best Defence: Complexity and Heuristics
CG '98 Proceedings of the First International Conference on Computers and Games
GIB: steps toward an expert-level bridge-playing program
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Machine Learning
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Making a decision, an agent must consider how his outcome can be influenced by possible actions of other agents. A 'best defense model' for games involving uncertainty assumes usually that the opponents know everything about the actual situation and the player's plans for certain. In this paper it's argued that the assumption results in algorithms that are too cautious to be good in many game settings. Instead, a 'reasonably good defense' model is proposed: the player should look for a best strategy against all the potential actions of the opponents, still assuming that any opponent plays his best according to his actual knowledge. The defense model is formalized for the case of two-player zero-sum (adversary) games. Also, algorithms for decision-making against 'reasonably good defense' are proposed.The argument and the ideas are supported by the results of experiments with random zero-sum two-player games on binary trees.