1. An efficient automated negotiation strategy for complex environments
- Author
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Siqi Chen, Gerhard Weiss, RS: FSE DACS, DKE Scientific staff, Dept. of Advanced Computing Sciences, and RS: FSE DACS RAI
- Subjects
Computer science ,business.industry ,Multi-agent system ,media_common.quotation_subject ,Autonomous agent ,Machine learning ,computer.software_genre ,Negotiation ,Artificial Intelligence ,Control and Systems Engineering ,Robustness (computer science) ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,computer ,media_common - 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 ([email protected]?D) and Autoregressive Moving Average ([email protected]?MA). 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.
- Published
- 2013