Online learning about other agents in a dynamic multiagent system

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 its 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. 1 Introduction A dynamic multiagent system, loosely speaking, is a system that changes over time due to the interaction of multiple agents. Examples of such systems abound; no doubt many will be represented at this conference. Popular demonstration applications include robotic soccer [1], the predator-prey pursuit game ...