The nature of statistical learning theory
The nature of statistical learning theory
Artificial Intelligence Review - Special issue on lazy learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Efficient learning equilibrium
Artificial Intelligence
Exploring bidding strategies for market-based scheduling
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
Learning to Coordinate Efficiently: a model-based approach
Journal of Artificial Intelligence Research
Empirical mechanism design: methods, with application to a supply-chain scenario
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Learning payoff functions in infinite games
Machine Learning
Methods for empirical game-theoretic analysis
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
An analysis of the 2004 supply chain management trading agent competition
AMEC'05 Proceedings of the 2005 international conference on Agent-Mediated Electronic Commerce: designing Trading Agents and Mechanisms
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We consider a class of games with real-valued strategies and payoff information available only in the form of data from a given sample of strategy profiles. Solving such games with respect to the underlying strategy space requires generalizing from the data to a complete payoff-function representation. We address payoff-function learning as a standard regression problem, with provision for capturing known structure (symmetry) in the multiagent environment. To measure learning performance, we consider the relative utility of prescribed strategies, rather than the accuracy of payoff functions per se. We demonstrate our approach and evaluate its effectiveness on two examples: a two-player version of the first-price sealed-bid auction (with known analytical form), and a five-player marketbased scheduling game (with no known solution).