Abstract
This paper presents an intelligent offer policy in a negotiation environment, in which each agent involved learns the preferences of its opponent in order to improve its own performance. Each agent must also be able to detect drifts in the opponent’s preferences so as to quickly adjust itself to their new offer policy. For this purpose, two simple learning techniques were first evaluated: (i) based on instances (IB3) and (ii) based on Bayesian Networks. Additionally, as its known that in theory group learning produces better results than individual/single learning, the efficiency of IB3 and Bayesian classifier groups were also analyzed. Finally, each decision model was evaluated in moments of concept drift, being the drift gradual, moderate or abrupt. Results showed that both groups of classifiers were able to effectively detect drifts in the opponent’s preferences.
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
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based Learning Algorithms. Machine Learning 6(1), 37–66 (1991)
Coehoorn, R.M., Jennings, N.R.: Learning an Opponent’s Preferences to Make Effective Multi-Issue Negotiation Trade-Offs. In: Proc. of the Sixth International Conference on Electronic Commerce, pp. 59–68 (2004)
Norsys Software Corp., Netica-j manual (2009), http://www.norsys.com/neticaj/docs/netica#jman.pdf
Enembreck, F., Avila, B.C., Scalabrin, E.E., Barthès, J.-P.: Drifting Negotiations. Applied Artificial Intelligence 21(9), 861–881 (2007)
Enembreck, F., Tacla, C.A., Barthès, J.P.: Learning Negotiation Policies Using Ensemble-Based Drift Detection Techniques. International Journal of Artificial Intelligence Tools 18(2), 173–196 (2008)
Pearl, J.: Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann, San Mateo (1988)
Hindriks, K., Tykhonov, D.: Opponent modelling in automated multi-issue negotiation using bayesian learning. In: AAMAS’08: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 331–338 (2008)
Kersten, G.E., Michalowski, W., Szpakowicz, S., Koperczak, Z.: Restructurable representations of negotiation. Manage. Sc. 37(10), 1269–1290 (1991)
Klinkenberg, R., Renz, I.: Adaptive Information Filtering: Learning in the Presence of Concept Drifts. In: ICML-98, pp. 33–40 (1998)
Kolter, J.Z., Maloof, M.A.: Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift, p. 123. IEEE Computer Society Press, Los Alamitos (2003)
Littlestone, N., Warmuth, M.: The Weighted Majority algorithm. Information and Computation 108, 212–261 (1994)
Opitz, D., Maclin, R.: Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research 11, 169–198 (1999)
Ruggeri, F., Faltin, F., Kenett, R.: Bayesian Networks. Encyclopedia of Statistics in Quality & Reliability. Wiley & Sons, Chichester (2007)
Russell, S., Norving, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Englewood Cliffs (2004)
Zeng, D., Sycara, K.: Bayesian learning in negotiation. International Journal of Human-Computer Studies 48(1), 125–141 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nalepa, G.M., Ávila, B.C., Enembreck, F., Scalabrin, E.E. (2010). Learning Negotiation Policies Using IB3 and Bayesian Networks. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2010. IDEAL 2010. Lecture Notes in Computer Science, vol 6283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15381-5_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-15381-5_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15380-8
Online ISBN: 978-3-642-15381-5
eBook Packages: Computer ScienceComputer Science (R0)