Communications of the ACM
Graphical Models for Game Theory
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Computing Nash equilibria of action-graph games
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Computing correlated equilibria in multi-player games
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Multi-agent influence diagrams for representing and solving games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Dimensions of complexity of intelligent agents
PCAR '06 Proceedings of the 2006 international symposium on Practical cognitive agents and robots
On the complexity of Nash equilibria of action-graph games
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Computing optimal randomized resource allocations for massive security games
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Computational analysis of perfect-information position auctions
Proceedings of the 10th ACM conference on Electronic commerce
Computing pure Nash equilibria in symmetric action graph games
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Temporal action-graph games: a new representation for dynamic games
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Pure Nash equilibria: complete characterization of hard and easy graphical games
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Decentralized MDPs with sparse interactions
Artificial Intelligence
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Action-Graph Games (AGGs) (Bhat & Leyton-Brown 2004) are a fully expressive game representation which can compactly express strict and context-specific independence and anonymity structure in players' utility functions. We present an efficient algorithm for computing expected payoffs under mixed strategy profiles. This algorithm runs in time polynomial in the size of the AGG representation (which is itself polynomial in the number of players when the in-degree of the action graph is bounded). We also present an extension to the AGG representation which allows us to compactly represent a wider variety of structured utility functions.