CAREER: Decision Making and Learning in Dynamic Multiagent Systems

University Of Rochester, Rochester NY

Investigators

Abstract

This project investigates two important elements in intelligent multiagent systems: decision making and learning. Based on a theoretical model of dynamic games, the PI will investigate a new optimality criterion for decision making. Second, the PI will define a non-stationary strategy within a dynamic game framework, providing a language to describe the policy choices. Third, the PI will provide a framework to describe the interaction between learning and action choice, and redefine the concept of online learning. Fourth, the PI will search for efficient online learning algorithms to reach an optimal non-stationary strategy. Fifth, this research on non-stationary strategy learning will be linked to research on models for dynamic single-agent systems. Finally, the proposed methodology and algorithms will be implemented in agent applications for E-commerce domains and computer games. Potential applications include bidding agents for online auctions, pricebots for online retailers and computer characters for strategic games. The proposed research is integrated with the PI's education plan, which focuses on promoting the concept of intelligence for multiagent systems. The PI will teach decision making and learning in dynamic games in her Ph.D. seminar class, and construct a graduate course on agent design for multiagent systems and their applications in electronic commerce.