CS Colloq: Strategy Selection for Noisy Empirical Game Models

Title: Strategy Selection for Noisy Empirical Game ModelsSpeaker: Chris Kiekintveld (UMICH)Abstract:
Game theory offers tools for principled analysis of multi-agent systems. However, many potential applications are not amenable to conventional analytic approaches due to the size of the strategy space, payoff uncertainty, and other complications. I will introduce an alternative approach that uses empirical methods (e.g. simulation) as the basis for modeling and reasoning about the game. I illustrate this methodology with an application to the Trading Agent Competition Supply Chain Management game. The use of empirical models raises a number of challenging research questions. Among them is how players should modify their analysis to account for the uncertainty inherent in their observations. The remainder of my talk focuses on this question, evaluating several algorithms for selecting strategies based on noisy empirical game models.Bio:
Chris Kiekintveld is a Ph.D. candidate at the University of Michigan, working with Michael Wellman. His primary research interest is strategic reasoning in multi-agent systems, including both agent design and mechanism design applications. He is an active participant in the Trading Agent Competition as a lead developer for Deep Maize, one of the most successful agents in the supply chain management game.

Location: Vivian Hall of Engineering (VHE) - 217

Audiences: Everyone Is Invited

Contact: CS Colloquia