Toyota AI Seminar: Don’t Ask, Don’t Tell: Strategies for Modeling Agents in Cooperative Multiagent Systems
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Effective coordination among cooperating agents typically improves with greater mutual awareness. However, the costs and delays in achieving and maintaining mutual awareness, and reasoning about detailed models of others’ beliefs, goals, and plans, can impede the responsiveness and effectiveness of a multiagent system. That is, cooperating agents can sometimes benefit from intentionally not knowing aspects about each other, and not revealing some information about themselves. In this talk, I will describe some of my students’ previous and ongoing research that has explored the uses of abstraction and selective communication to achieve effective coordination without excessive overhead and without unnecessarily constraining individuals’ actions.
Ed Durfee is a Professor of Computer Science and Engineering, and of Information, at the University of Michigan, where he has served on the faculty for over 20 years. His research focuses on developing representations and algorithms for multiagent planning, scheduling, and coordination, with applications that include cooperative robotics, service-oriented computing, and cognitive orthotics. He received his AB degree from Harvard University and his PhD from the University of Massachusetts.