UTCS AI Colloquia - Keith Sullivan, George Mason University, "Hierarchical Multiagent Learning from Demonstration"

Contact Name: 
Karl Pichotta
Location: 
PAI 3.14
Date: 
Apr 12, 2013 11:00am - 12:00pm

Signup Schedule: http://apps.cs.utexas.edu/talkschedules/cgi/list_events.cgi

Talk Audience: UTCS Faculty, Grads, Undergrads, Other Interested Parties

Host:  Peter Stone

Talk Abstract: Developing agent behaviors is often a tedious, time-consuming task consisting of repeated code, test, and debug cycles. Despite the difficulties, complex agent behaviors have been developed, but they required significant programming ability. In this talk, I'll present an alternative approach: training via learning from demonstration. The system, Hierarchical Training of Agent Behavior (HiTAB), iteratively learns agent behaviors represented as hierarchical finite state automata. By manually decomposing complex behaviors into simpler sub-behaviors, HiTAB requires a limited number of examples. While this places HiTAB closer to programming by demonstration rather than machine learning, it allows novice users to rapidly train complex agent behaviors.

The multiagent situation presents difficulties due to the large, high-dimensional learning space. Furthermore, as a supervised learning method, HiTAB requires that agents be told which micro-level behaviors to perform in various situations. This is sufficient for a single agent since there is no distinction between micro- and macro-level behaviors. However, in the multiagent setting, an experimenter only knows which macro-level behavior to achieve, and not the associated micro-level behaviors, which presents a difficult inverse problem. HiTAB uses an agent hierarchy to decrease the gap between micro- and macro-level behaviors. This hierarchy permits rapid training of (potentially) large numbers of agents using a small number of samples. 

I will present results from simulation and real robots (including RoboCup) demonstrating HiTAB's wide applicability.

Speaker Bio: Keith Sullivan is a PhD candidate in computer science at George Mason University, supervised by Professor Sean Luke. His dissertation developed methods to train cooperative multiagent behaviors using learning from demonstration. His research interests include robotics, multiagent learning, and stochastic optimization. He helped develop MASON, an open-source Java multiagent simulation toolkit, and ECJ, a Java-based, open-source evolutionary computation toolkit. During his PhD, he received grants for extended visits to Minoru Asada in Osaka and Danielle Nardi in Rome. He is the creator and leader of the RoboPatriots, GMU's humanoid robot soccer team, and co-developer of the FlockBots, an open-source differential drive robot meant for embodied multiagent systems research.

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