A Monte-Carlo approach to uncertain inference
Artificial intelligence and its applications
An adaption of proof-planning to declarer play in bridge
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Search in games with incomplete information: a case study using Bridge card play
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
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Game playing (invited talk): the next moves
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
Optimal Play against Best Defence: Complexity and Heuristics
CG '98 Proceedings of the First International Conference on Computers and Games
A Defense Model for Games with Incomplete Information
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
GIB: imperfect information in a computationally challenging game
Journal of Artificial Intelligence Research
Learning without human expertise: a case study of the double dummy bridge problem
IEEE Transactions on Neural Networks
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
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We examine three heuristic algorithms for games with imperfect information: Monte-carlo sampling, and two new algorithms we call vector minimaxing and payoff-reduction minimaxing. We compare these algorithms theoretically and experimentally, using both simple game trees and a large database of problems from the game of Bridge. Our experiments show that the new algorithms both out-perform Monte-carlo sampling, with the superiority of payoff-reduction minimaxing being especially marked. On the Bridge problem set, for example, Monte-carlo sampling only solves 66% of the problems, whereas payoff-reduction minimaxing solves over 95%. This level of performance was even good enough to allow us to discover five errors in the expert text used to generate the test database.