Artificial Intelligence - Special issue on knowledge representation
IJCAI '99 Proceedings of the Sixteenth International Joint 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
Exploring bidding strategies for market-based scheduling
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
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
Empirical mechanism design: methods, with application to a supply-chain scenario
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Generating trading agent strategies: analytic and empirical methods for infinite and large games
Generating trading agent strategies: analytic and empirical methods for infinite and large games
Empirical game-theoretic analysis of the TAC Supply Chain game
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Methods for empirical game-theoretic analysis
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Approximate strategic reasoning through hierarchical reduction of large symmetric games
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
The supply chain trading agent competition
Electronic Commerce Research and Applications
Selecting strategies using empirical game models: an experimental analysis of meta-strategies
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Generalization risk minimization in empirical game models
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Generalised fictitious play for a continuum of anonymous players
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
What the 2007 TAC Market Design Game tells us about effective auction mechanisms
Autonomous Agents and Multi-Agent Systems
Evolutionary mechanism design: a review
Autonomous Agents and Multi-Agent Systems
Strategy exploration in empirical games
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Probabilistic analysis of simulation-based games
ACM Transactions on Modeling and Computer Simulation (TOMACS)
An algorithmic game theory study of wholesale electricity markets based on central auction
Integrated Computer-Aided Engineering - Multi-Agent Systems for Energy Management
Strategic analysis with simulation-based games
Winter Simulation Conference
False-name bidding in first-price combinatorial auctions with incomplete information
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Learning equilibria of games via payoff queries
Proceedings of the fourteenth ACM conference on Electronic commerce
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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When exploring a game over a large strategy space, it may not be feasible or cost-effective to evaluate the payoff of every relevant strategy profile. For example, determining a profile payoff for a procedurally defined game may require Monte Carlo simulation or other costly computation. Analyzing such games poses a search problem, with the goal of identifying equilibrium profiles by evaluating payoffs of candidate solutions and potential deviations from those candidates. We propose two algorithms, applicable to distinct models of the search process. In the revealed-payoff model, each search step determines the exact payoff for a designated pure-strategy profile. In the noisy-payoff model, a step draws a stochastic sample corresponding to such a payoff. We compare our algorithms to previous proposals from the literature for these two models, and demonstrate performance advantages.