Multiagent and Economic Systems

Environments with multiple autonomous agents present specialopportunities and pose distinct challenges for design and analysis of AI systems. An individual agent may coordinate with others to improve performance through intelligent selection of physical, communicative, and/or computational actions. The agent may also reason strategically, to predict what the other agents may do based on their presumed self-interests. A multiagent environment is effectively a social system, and thus analyzing multiagent behavior can often be informed by social science. Multiagent systems research at Michigan considers all perspectives, from individual agent to social designer.

We design planning and learning algorithms suitable for multiagent contexts, and methods for analyzing networks of agents as organizations, economies, and societies. Our work also spans the range from theory to practice. We conduct fundamental research in distributed coordination, algorithmic game theory, and social computing, and apply our techniques to real-world problems in areas such as healthcare, electronic commerce, and finance.

CSE Faculty

Baveja, Satinder Singh
Durfee, Edmund H
Schoenebeck, Grant
Wellman, Michael

Related Labs, Centers, and Groups
Distributed Intelligent Agents Group