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
Graphical Models for Game Theory
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Probabilistic reasoning for complex systems
Probabilistic reasoning for complex systems
Computing Nash equilibria of action-graph games
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Computational analysis of perfect-information position auctions
Proceedings of the 10th ACM conference on Electronic commerce
A polynomial-time algorithm for action-graph games
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
A continuation method for Nash equilibria in structured games
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Causal independence for probability assessment and inference using Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper we introduce temporal action graph games (TAGGs), a novel graphical representation of imperfect-information extensive form games. We show that when a game involves anonymity or context-specific utility independencies, its encoding as a TAGG can be much more compact than its direct encoding as a multiagent influence diagram (MAID). We also show that TAGGs can be understood as indirect MAID encodings in which many deterministic chance nodes are introduced. We provide an algorithm for computing with TAGGs, and show both theoretically and empirically that our approach improves significantly on the previous state of the art.