Complete inflation and perfect recall in extensive games
International Journal of Game Theory
Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to algorithms
Fast algorithms for finding randomized strategies in game trees
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Learning models of other agents using influence diagrams
UM '99 Proceedings of the seventh international conference on User modeling
Handbook of Computational Economics
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UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Probabilistic Models for Agent's Beliefs and Decisions
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Evaluating Influence Diagrams using LIMIDs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
SAFECOMP'06 Proceedings of the 25th international conference on Computer Safety, Reliability, and Security
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The traditional representations of games using the extensive form or the strategic (normal) form obscure much of the structure that is present in real-world games. In this paper, we propose a new representation language for general multi-player noncooperative games --- multi-agent influence diagrams (MAIDs). This representation extends graphical models for probability distributions to a multi-agent decision-making context. The basic elements in the MAID representation are variables rather than strategies (as in the normal form) or events (as in the extensive form). They can thus explicitly encode structure involving the dependence relationships among variables. As a consequence, we can define a notion of strategic relevence of one decision variable to another. D' is strategically relevant to D if, to optimize the decision rule at D, the decision maker needs to take into consideration the decision rule at D. We provide a sound and complete graphical criterion for determining strategic relevance. We then show how strategic relevance can be used to detect structure in games, allowing large games to be broken up into a set of interacting smaller games, which can be solved in sequence. We show that this decomposition can lead to substantial savings in the computational cost of finding Nash equilibria in these games.