Abstract
A complex and challenging bilateral negotiation environment for rational autonomous agents is where agents negotiate multi-issue contracts in unknown application domains against unknown opponents under real-time constraints. In this paper we present a novel negotiation strategy called EMAR for this kind of environment which is based on a combination of Empirical Mode Decomposition (EMD) and Autoregressive Moving Average (ARMA). EMAR enables a negotiating agent to adjust its target utility and concession rate adaptively in real-time according to the behavior of its opponent. The experimental results show that this new strategy outperforms the best agents from the latest Automated Negotiation Agents (ANAC) Competition in a wide range of application domains.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Baarslag, T., Hindriks, K., Jonker, C., Kraus, S., Lin, R.: The First Automated Negotiating Agents Competition (ANAC 2010). In: Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T. (eds.) New Trends in Agent-Based Complex Automated Negotiations. SCI, vol. 383, pp. 113–135. Springer, Heidelberg (2012)
Box, G., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis: Forecasting and Control, 3rd edn. Prentice-Hall (1994)
Brzostowski, J., Kowalczyk, R.: Predicting partner’s behaviour in agent negotiation. In: Proceedings of the Fifth Int. Joint Conf. on Autonomous Agents and Multiagent Systems, AAMAS 2006, pp. 355–361. ACM, New York (2006)
Carbonneau, R., Kersten, G.E., Vahidov, R.: Predicting opponent’s moves in electronic negotiations using neural networks. Expert Syst. Appl. 34, 1266–1273 (2008)
Chen, S., Weiss, G.: An Efficient and Adaptive Approach to Negotiation in Complex Environments. In: Proceedings of the 20th European Conference on Artificial Intelligence (ECAI 2012), Montpellier, France. IOS Press (2012)
Coehoorn, R.M., Jennings, N.R.: Learning on opponent’s preferences to make effective multi-issue negotiation trade-offs. In: Proceedings of the 6th Int. Conf. on Electronic Commerce, ICEC 2004, pp. 59–68. ACM, New York (2004)
Hunag, N.E., Shen, Z., Long, S.R.: The empirical mode decomposition and the hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. R. Soc. Lond. A, 903–995 (1998)
Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for autonomous agents. Robotics and Autonomous Systems 24(4), 159–182 (1998)
Faratin, P., Sierra, C., Jennings, N.R.: Using similarity criteria to make issue trade-offs in automated negotiations. Artif. Intell. 142(2), 205–237 (2002)
Flandrin, P., Rilling, G., Gonçalvès, P., Basics, I.E.: Empirical mode decomposition as a filter bank. IEEE Signal Proc. Lett. 11, 112–114 (2004)
Hendrikx, M.: A survey of oppnent models in automated negotiation. Technical report, Delft University of Technology, The Netherlands (September 2011)
Hindriks, K., Jonker, C., Kraus, S., Lin, R., Tykhonov, D.: Genius: negotiation environment for heterogeneous agents. In: Proceedings of AAMAS 2009, pp. 1397–1398 (2009)
Hou, C.: Predicting agents tactics in automated negotiation. In: IEEE / WIC / ACM International Conference on Intelligent Agent Technology, pp. 127–133. IEEE Computer Society, Los Alamitos (2004)
Huang, N.E., Shen, S.S.P.: Hilbert-Huang transform and its applications. World Scientific (2005)
Huang, N.E., Wu, M.-L., Qu, W., Long, S.R., Shen, S.S.P.: Applications of hilbert-huang transform to non-stationary financial time series analysis. Appl. Stoch. Models Bus. Ind. 19(3), 245–268 (2003)
Lai, G., Li, C., Sycara, K., Giampapa, J.: Literature review on multi-attribute negotiations. Technical Report CMU-RI-TR-04-66, Robotics Institute, Pittsburgh, PA (December 2004)
Lin, R., Kraus, S., Wilkenfeld, J., Barry, J.: Negotiating with bounded rational agents in environments with incomplete information using an automated agent. Artif. Intell. 172, 823–851 (2008)
Raiffa, H.: The art and science of negotiation. Harvard University Press, Cambridge (1982)
Rubinstein, A.: Perfect equilibrium in a bargaining model. Econometrica 50(1), 97–109 (1982)
Saha, S., Biswas, A., Sen, S.: Modeling opponent decision in repeated one-shot negotiations. In: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2005, pp. 397–403. ACM, New York (2005)
Williams, C., Robu, V., Gerding, E., Jennings, N.: Using gaussian processes to optimise concession in complex negotiations against unknown opponents. In: Proceedings of the 22nd Internatioanl Joint Conference on Artificial Intelligence. AAAI Press (2011)
Yu, L., Wang, S., Lai, K.K.: Forecasting crude oil price with an emd-based neural network ensemble learning paradigm. Energy Economics 30(5), 2623–2635 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, S., Weiss, G. (2012). A Novel Strategy for Efficient Negotiation in Complex Environments. In: Timm, I.J., Guttmann, C. (eds) Multiagent System Technologies. MATES 2012. Lecture Notes in Computer Science(), vol 7598. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33690-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-33690-4_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33689-8
Online ISBN: 978-3-642-33690-4
eBook Packages: Computer ScienceComputer Science (R0)