Online Learning about Other Agents in a Dynamic Multiagent System

Junling Hu and Michael P. Wellman
Artificial Intelligence Laboratory
University of Michigan
Ann Arbor, MI 48109-2110, USA
{junling, wellman}@umich.edu
http://ai.eecs.umich.edu/people/{junling,wellman}

Abstract:

We analyze the problem of learning about other agents in a class of dynamic multiagent systems, where performance of the primary agent depends on behavior of the others. We consider an online version of the problem, where agents must learn models of the others in the course of continual interactions. We implement various levels of recursive model in a simulated double auction market. Our experiments show that performance of an agent can be quite sensitive to it assumptions about the policies of other agents, and (not surprisingly), when there is substantial uncertainty about the level of sophistication of other agents, minimizing assumptions might be the best policy.

Keywords: multiagent learning, computational markets