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. © 2012 Springer-Verlag.
CITATION STYLE
Chen, S., & Weiss, G. (2012). A novel strategy for efficient negotiation in complex environments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7598 LNAI, pp. 68–82). https://doi.org/10.1007/978-3-642-33690-4_8
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