Learning the Structure of Utility Graphs Used in Multi-issue Negotiation through Collaborative Filtering
- 371 Downloads
Graphical utility models represent powerful formalisms for modeling complex agent decisions involving multiple issues . In the context of negotiation, it has been shown  that using utility graphs enables reaching Pareto-efficient agreements with a limited number of negotiation steps, even for high-dimensional negotiations involving complex complementarity/ substitutability dependencies between multiple issues. This paper considerably extends the results of , by proposing a method for constructing the utility graphs of buyers automatically, based on previous negotiation data. Our method is based on techniques inspired from item-based collaborative filtering, used in online recommendation algorithms. Experimental results show that our approach is able to retrieve the structure of utility graphs online, with a high degree of accuracy, even for highly non-linear settings and even if a relatively small amount of data about concluded negotiations is available.
Unable to display preview. Download preview PDF.
- 2.Chajewska, U., Koller, D.: Utilities as random variables: Density estimation and structure discovery. In: Proceedings of sixteenth Annual Conference on Uncertainty in Artificial Intelligence UAI 2000, pp. 63–71 (2000)Google Scholar
- 3.Debenham, J.K.: Bargaining with information. In: 3rd Int. Conf. on Autonomous Agents & Multi Agent Systems (AAMAS), New York, July 19-23, 2004, pp. 663–670 (2004)Google Scholar
- 6.Lin, R.: Bilateral multi-issue contract negotiation for task redistribution using a mediation service. In: Proc. Agent Mediated Electronic Commerce VI, New York, USA (2004)Google Scholar
- 7.Coehoorn, R.M., Jennings, N.R.: Learning an opponent’s preferences to make effective multi-issue negotiation tradeoffs. In: Proc. 6th Int Conf. on E-Commerce, Delft (2004)Google Scholar
- 9.Raiffa, H.: The art and science of negotiation. Harvard University Press, Cambridge, Massachussets USA (1982)Google Scholar
- 10.Robu, V., Somefun, D.J.A., La Poutré, J.A.: Modeling complex multi-issue negotiations using utility graphs. In: 4th Int. Conf. on Autonomous Agents & Multi Agent Systems (AAMAS), Utrecht, The Netherlands (2005) (to appear as full paper), http://homepages.cwi.nl/~robu/AAMAS05.pdf
- 11.Jennings, N., Fatima, S., Woolridge, M.: Optimal negotiation of multiple issues in incomplete information settings. In: 3rd Int. Conf. on Autonomous Agents & Multi Agent Systems (AAMAS), New York, pp. 1080–1087 (2004)Google Scholar
- 12.Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Tenth International WWW Conference (WWW10), Hong Kong (2001)Google Scholar
- 13.Somefun, D.J.A., Klos, T.B., La Poutré, J.A.: Online learning of aggregate knowledge about nonlinear preferences applied to negotiating prices and bundles. In: Proc. 6th Int Conf. on E-Commerce, Delft, pp. 361–370 (2004)Google Scholar