Abstract
There has been an increasing interest in automated negotiation and particularly negotiations that involve interdependent issues, known to yield complex nonlinear utility spaces. However, none of the proposed models was able to tackle the scaling problem as it commonly arises in realistic consensus making situations. In this paper we address this point by proposing a compact representation that minimizes the search complexity in this type of utility spaces. Our representation allows a modular decomposition of the issues and the constraints by mapping the utility space into an issue-constraint hyper-graph with the underlying interdependencies. Exploring the utility space reduces then to a message passing mechanism along the hyper-edges by means of utility propagation. We experimentally evaluate the model using parameterized random nonlinear utility spaces, showing that our mechanism can handle a large family of complex utility spaces by finding the optimal contracts, outperforming previous sampling-based approaches.
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
Kraus, S., Sycara, K., Evenchik, A.: Reaching agreements through argumentation: a logical model and implementation. Artificial Intelligence 104(1-2), 1–69 (1998)
Jennings, N.R.: An agent-based approach for building complex software systems. Commun. ACM 44(4), 35–41 (2001)
Krainin, M., An, B., Lesser, V.R.: An application of automated negotiation to distributed task allocation. In: IAT, pp. 138–145. IEEE Computer Society (2007)
Ke, W., Peng, Z., Yuan, Q., Hong, B., Chen, K., Cai, Z.: A method of task allocation and automated negotiation for multi robots. Journal of Electronics (China) 29(6), 541–549 (2012)
Fatima, S.S., Wooldridge, M., Jennings, N.R.: An agenda-based framework for multi-issue negotiation. Artificial Intelligence 152(1), 1–45 (2004)
Kraus, S.: Strategic Negotiation in Multiagent Environments. MIT Press, Cambridge (2001)
Ito, T., Klein, M., Hattori, H.: A multi-issue negotiation protocol among agents with nonlinear utility functions. Multiagent and Grid Systems 4(1), 67–83 (2008)
Chajewska, U., Koller, D.: Utilities as random variables: Density estimation and structure discovery. In: Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI 2000), pp. 63–71 (2000)
Bacchus, F., Grove, A.: Graphical models for preference and utility. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, UAI 1995, pp. 3–10. Morgan Kaufmann Publishers Inc., San Francisco (1995)
Robu, V., Somefun, D.J.A., Poutre, J.L.: Modeling complex multi-issue negotiations using utility graphs. In: Proceedings of the 4th International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2005), pp. 280–287 (2005)
Marsa-Maestre, I., Lopez-Carmona, M.A., Velasco, J.R., de la Hoz, E.: Effective bidding and deal identification for negotiations in highly nonlinear scenarios. In: Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2009, vol. 2, pp. 1057–1064. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2009)
Fujita, K., Ito, T., Klein, M.: An approach to scalable multi-issue negotiation: Decomposing the contract space based on issue interdependencies. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2010, vol. 02, pp. 399–406. IEEE Computer Society, Washington, DC (2010)
Ito, T., Hattori, H., Klein, M.: Multi-issue negotiation protocol for agents: Exploring nonlinear utility spaces. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-2007), pp. 1347–1352 (2007)
Lopez-Carmona, M.A., Marsa-Maestre, I., De La Hoz, E., Velasco, J.R.: A region-based multi-issue negotiation protocol for nonmonotonic utility spaces. Computational Intelligence 27(2), 166–217 (2011)
Marsa-Maestre, I., Lopez-Carmona, M., Carral, J., Ibanez, G.: A recursive protocol for negotiating contracts under non-monotonic preference structures. Group Decision and Negotiation 22(1), 1–43 (2013)
Fujita, K., Ito, T., Klein, M.: A secure and fair negotiation protocol in highly complex utility space based on cone-constraints. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2009, vol. 02, pp. 427–430. IEEE Computer Society, Washington, DC (2009)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Hadfi, R., Ito, T. (2014). Modeling Complex Nonlinear Utility Spaces Using Utility Hyper-Graphs. In: Torra, V., Narukawa, Y., Endo, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2014. Lecture Notes in Computer Science(), vol 8825. Springer, Cham. https://doi.org/10.1007/978-3-319-12054-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-12054-6_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12053-9
Online ISBN: 978-3-319-12054-6
eBook Packages: Computer ScienceComputer Science (R0)