Quantifying the utility of building agent models: An experimental study

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 a way 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 leastto most-informed one in order to evaluate the lowerand 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.