Quantifying the Utility of Building Agents Models: An Experimental Study

Abstract

This paper presents some of our experimental work in quantifying the value of building models about other agents using no more than the observation of others ' behavior. We view agent modeling as an iterative and gradual process, where every new piece of information about a particular agent is analyzed in such away that the model of the agent is further re ned. We present our bayesian-modeler agent which updates his models about the others using a bayesian updating mechanism. Then, he plays in a rational way using a decision-theoretic approach based on the probabilistic models that he is learning. We experimentally explore a range of strategies from least- to most-informed one in order to evaluate the lower- and upper-limits of the modeler agent performance. We have been running experiments in our test bed, the Meeting Scheduling Game, which resembles some characteristics of the distributed meeting scheduling problem.