Learning and Predicting Dynamic Behavior with Graphical Multiagent Models

Factored models of multiagent systems address the complexity of joint behavior by exploiting locality in agent interactions. History-dependent graphical multiagent models (hGMMs) further capture dynamics by conditioning behavior on history. The challenges of modeling real human behavior motivated us to extend the hGMM representation by distinguishing two types of agent interactions. This distinction opens the opportunity for learning dependence networks that are different from given graphical structures representing observed agent interactions. We propose a greedy algorithm for learning hGMMs from time-series data, inducing both graphical structure and parameters. Our empirical study employs human-subject experiment data for a dynamic consensus scenario, where agents on a network attempt to reach a unanimous vote. We show that the learned hGMMs directly expressing joint behavior outperform alternatives in predicting dynamic human voting behavior, and end-game vote results. Analysis of learned graphical structures reveals patterns of action dependence not directly reflected in the original experiment networks. Disciplines Computer Sciences Comments Duong, Q., Wellman, M., Singh, S., & Kearns, M., Learning and Predicting Dynamic Behavior with Graphical Multiagent Models, 11th International Conference on Autonomous Agents and Multiagent Systems, June 2012 http://www.ifaamas.org/Proceedings/aamas2012/resources/fullpapers.html#s-2C This working paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/636 Learning and Predicting Dynamic Networked Behavior with Graphical Multiagent Models Quang Duong† Michael P. Wellman† Satinder Singh† Michael Kearns∗ †Computer Science and Engineering, University of Michigan ∗Computer and Information Science, University of Pennsylvania