Gaining competitive advantage through learning agent models

Abstract

We present our bayesian-modeler agent which uses a probabilistic approach for agent modeling. It learns models about the others using a bayesian mechanism and then it plays in a rational way using a decision-theoretic approach. We also describe our empirical study on evaluating the competitive advantage of our modeler agent. We explore a range of strategies from the least- to most-informed one in order to evaluate the lower- and upper-limits of a modeler agent's performance. For comparison purposes, we also developed and experimented with other different modeler agents using reinforcement learning techniques. Our experimental results showed how an agent that learns models about the others, using our probabilistic approach, reach almost the optimal performance of the oracle agent. Our experiments have also shown that a modeler agent using a reinforcement learning technique have a performance not as good as the bayesian modeler' performance. However, it could be competitive under different assumptions and restrictions. © Springer-Verlag Berlin Heidelberg 2004.