Learning regular sets from queries and counterexamples
Information and Computation
The complexity of pure Nash equilibria
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Bounds for the convergence rate of randomized local search in a multiplayer load-balancing game
Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
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
Using tabu best-response search to find pure strategy nash equilibria in normal form games
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Fast convergence to Wardrop equilibria by adaptive sampling methods
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Learning payoff functions in infinite games
Machine Learning
Approximating nash equilibria using small-support strategies
Proceedings of the 8th ACM conference on Electronic commerce
Distributed Selfish Load Balancing
SIAM Journal on Computing
Searching for approximate equilibria in empirical games
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Nash equilibria in discrete routing games with convex latency functions
Journal of Computer and System Sciences
Approximate Nash Equilibria for Multi-player Games
SAGT '08 Proceedings of the 1st International Symposium on Algorithmic Game Theory
A note on approximate Nash equilibria
Theoretical Computer Science
On oblivious PTAS's for nash equilibrium
Proceedings of the forty-first annual ACM symposium on Theory of computing
Methods for empirical game-theoretic analysis
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
The Complexity of Computing a Nash Equilibrium
SIAM Journal on Computing
Learning graphical game models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Convergence time to Nash equilibria
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
Nashification and the coordination ratio for a selfish routing game
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
Strategy exploration in empirical games
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
On learning algorithms for nash equilibria
SAGT'10 Proceedings of the Third international conference on Algorithmic game theory
Graphical models for game theory
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
On the communication complexity of approximate nash equilibria
SAGT'12 Proceedings of the 5th international conference on Algorithmic Game Theory
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A recent body of experimental literature has studied empirical game-theoretical analysis, in which we have partial knowledge of a game, consisting of observations of a subset of the pure-strategy profiles and their associated payoffs to players. The aim is to find an exact or approximate Nash equilibrium of the game, based on these observations. It is usually assumed that the strategy profiles may be chosen in an on-line manner by the algorithm. We study a corresponding computational learning model, and the query complexity of learning equilibria for various classes of games. We give basic results for bimatrix and graphical games. Our focus is on symmetric network congestion games. For directed acyclic networks, we can learn the cost functions (and hence compute an equilibrium) while querying just a small fraction of pure-strategy profiles. For the special case of parallel links, we have the stronger result that an equilibrium can be identified while only learning a small fraction of the cost values.