A bound on the proportion of pure strategy equilibria in generic games
Mathematics of Operations Research
On the NP-completeness of finding an optimal strategy in games with common payoffs
International Journal of Game Theory
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Theory of Globally Convergent Probability-One Homotopies for Nonlinear Programming
SIAM Journal on Optimization
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
Multi-agent algorithms for solving graphical games
Eighteenth national conference on Artificial intelligence
Run the GAMUT: A Comprehensive Approach to Evaluating Game-Theoretic Algorithms
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Computing Nash equilibria of action-graph games
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
On the computational complexity of Nash equilibria for (0, 1) bimatrix games
Information Processing Letters
A continuation method for Nash equilibria in structured games
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Complexity results about Nash equilibria
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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
Stochastic search methods for nash equilibrium approximation in simulation-based games
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
On the complexity of constrained Nash equilibria in graphical games
Theoretical Computer Science
Journal of Artificial Intelligence Research
A sampling-based approach to computing equilibria in succinct extensive-form games
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
A general framework for computing optimal correlated equilibria in compact games
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
Computing nash equilibria of action-graph games via support enumeration
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
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Structured game representations have recently attracted interest as models for multi-agent artificial intelligence scenarios, with rational behavior most commonly characterized by Nash equilibria. This paper presents efficient, exact algorithms for computing Nash equilibria in structured game representations, including both graphical games and multi-agent influence diagrams (MAIDs). The algorithms are derived from a continuation method for normal-form and extensive-form games due to Govindan and Wilson; they follow a trajectory through a space of perturbed games and their equilibria, exploiting game structure through fast computation of the Jacobian of the payoff function. They are theoretically guaranteed to find at least one equilibrium of the game, and may find more. Our approach provides the first efficient algorithm for computing exact equilibria in graphical games with arbitrary topology, and the first algorithm to exploit fine-grained structural properties of MAIDs. Experimental results are presented demonstrating the effectiveness of the algorithms and comparing them to predecessors. The running time of the graphical game algorithm is similar to, and often better than, the running time of previous approximate algorithms. The algorithm for MAIDs can effectively solve games that are much larger than those solvable by previous methods.