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- W2118992023 abstract "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 (EM̲D) and Autoregressive Moving Average (AR̲MA). 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." @default.
- W2118992023 created "2016-06-24" @default.
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- W2118992023 date "2013-11-01" @default.
- W2118992023 modified "2023-09-24" @default.
- W2118992023 title "An efficient automated negotiation strategy for complex environments" @default.
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- W2118992023 doi "https://doi.org/10.1016/j.engappai.2013.08.012" @default.
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