Representations and solutions for game-theoretic problems
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Conjectural Equilibrium in Multiagent Learning
Machine Learning
Computing sequential equilibria for two-player games
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
The complexity of computing a Nash equilibrium
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Learning equilibrium as a generalization of learning to optimize
Artificial Intelligence
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Simple search methods for finding a Nash equilibrium
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Mixed-integer programming methods for finding Nash equilibria
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Extensive--form games with heterogeneous populations
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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The Nash equilibrium is the most commonly adopted solution concept for non-cooperative interaction situations. However, it underlays on the assumption of common information that is hardly verified in many practical situations. When information is not common, the appropriate game theoretic solution concept is the self-confirming equilibrium. It requires that every agent plays the best response to her beliefs and that the beliefs are correct on the equilibrium path. We present, to the best of our knowledge, the first study on the computation of a self-confirming equilibrium for two-player extensive-form games. We provide algorithms, we analyze the computational complexity, and we experimentally evaluate the performance of our algorithms in terms of computational time.