CS Colloq: Learning^3: Multi-Agent, Teacher-Agent, and Tutor-Student

Title: Learning^3: Multi-Agent, Teacher-Agent, and Tutor-StudentSpeaker: Dr. Yu-Han Chang (ISI)Abstract:
Learning is crucial aspect of any intelligent agent. The bulk of this talk with focus on our results in multi-agent learning, where agents must learn to adapt in environments populated with other adaptive, autonomous agents. I'll also spend some time briefly describing new projects in teachable agents, where agents can learn more rapidly by receiving interactive human instruction, and adaptive tutoring systems, where the tutoring system must learn to adapt to differing student capabilities and styles. In multi-agent environments, learning must account for the adaptive nature of the other agents. Traditional models such as MDPs, POMDPs, and game theoretic equilibria each have their shortcomings in this domain: e.g. the environment is not Markov, or the other agents may not be entirely rational. Regret is a principled framework for evaluating the performance of multi-agent learning algorithms, and regret-minimizing algorithms offer a good approach to this domain, one that does not need to make strong assumptions regarding expected types of opponents. I'll describe an algorithm that exhibits good performance against a wide range of possible opponents, and guarantees low regret against any arbitrary opponent.Biography:
Dr. Yu-Han Chang is a Computer Scientist at the Information Sciences Institute of the University of Southern California. His current research interests range from reinforcement learning and game theory to natural language understanding and interactive games. Recent and ongoing projects include using machine learning to improve education, "learning by noticing", planning in continuous battle spaces, training intelligent agents via interactive games, and developing no-regret algorithms for learning in non-cooperative domains. Dr. Chang holds undergraduate degrees in Mathematics and Economics, as well as a S.M. in Computer Science, from Harvard University. He received his Ph.D. in Electrical Engineering and Computer Science from MIT, where he developed algorithms for multi-agent learning in the context of machine learning and game theory.

Location: Henry Salvatori Computer Science Center (SAL) - 322

Audiences: Everyone Is Invited

Contact: CS Colloquia