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.
This research has been supported in part by the ITESM Research Chair CAT-011.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tambe, M., Rosenbloom, P.: Architectures for agents that track other agents in multi-agent worlds. In: Tambe, M., Müller, J., Wooldridge, M.J. (eds.) IJCAI-WS 1995 and ATAL 1995. LNCS, vol. 1037. Springer, Heidelberg (1996)
Gmytrasiewicz, P.: On reasoning about other agents. In: Tambe, M., Müller, J., Wooldridge, M.J. (eds.) IJCAI-WS 1995 and ATAL 1995. LNCS (LNAI), vol. 1037. Springer, Heidelberg (1996)
Suryadi, D., Gmytrasiewicz, P.: Learning models of other agents using influence diagrams. In: IJCAI 1999 Workshop on Agents Learning About, From, and With other Agents. John Wiley and Sons, Chichester (1999)
Vidal, J., Durfee, E.: Using recursive agent models effectively. In: Tambe, M., Müller, J., Wooldridge, M.J. (eds.) IJCAI-WS 1995 and ATAL 1995. LNCS (LNAI), vol. 1037. Springer, Heidelberg (1996)
Vidal, J., Durfee, E.: Agents learning about agents: A framework and analysis. In: AAAI 1997 Workshop on Multiagent Learning (1997)
Gmytrasiewicz, P., Noh, S., Kellogg, T.: Bayesian update of recursive agents models. Journal of User Modeling and User-Adapted Interaction 8, 49–69 (1998)
Zeng, D., Sycara, K.: Bayesian learning in negotiation. Internation Journal in Human-Computer Systems 48, 125–141 (1998)
Garrido, L., Sycara, K., Brena, R.: Quantifying the utility of building agents models: An experimental study. In: Learning Agents Workshop at the Fourth International Conference on Autonomous Agents, Agents 2000 (2000)
Garrido, L., Brena, R., Sycara, K.: On measuring the usefulness of modeling in a competitive and cooperative environment. In: Collaborative Learning Agents Workshop at the AAAI 2002 Spring Symposium (2002)
Garrido, L., Brena, R., Sycara, K.: Towards modeling other agents: A simulationbased study. In: Sichman, J.S., Conte, R., Gilbert, N. (eds.) MABS 1998. LNCS, vol. 1534, pp. 210–225. Springer, Heidelberg (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Garrido, L., Brena, R., Sycara, K. (2004). Gaining Competitive Advantage Through Learning Agent Models. In: Lemaître, C., Reyes, C.A., González, J.A. (eds) Advances in Artificial Intelligence – IBERAMIA 2004. IBERAMIA 2004. Lecture Notes in Computer Science(), vol 3315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30498-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-30498-2_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23806-5
Online ISBN: 978-3-540-30498-2
eBook Packages: Springer Book Archive