An efficient automated negotiation strategy for complex environments

S. Chen*, G. Weiss

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


A complex and challenging bilateral negotiation environment for rational autonomous agents is where agents negotiate multi-issue contracts in unknown application domains with unknown opponents under real-time constraints. In this paper we present a negotiation strategy called EMAR for this kind of environment that relies on a combination of Empirical Mode Decomposition (EMD) and Autoregressive Moving Average (ARMA). EMAR enables a negotiating agent to acquire an opponent model and to use this model for adjusting its target utility in real-time on the basis of an adaptive concession-making mechanism. Experimental results show that EMAR outperforms best performing agents from the recent Automated Negotiating Agents Competitions (ANAC) in a wide range of application domains. Moreover, an analysis based on empirical game theory is provided that shows the robustness of EMAR in different negotiation contexts.
Original languageEnglish
Pages (from-to)2613-2623
Number of pages11
JournalEngineering Applications of Artificial Intelligence
Issue number10
Publication statusPublished - 2013

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