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• W3036684219 endingPage "100987" @default.
• W3036684219 startingPage "100987" @default.
• W3036684219 abstract "This paper aims to design an intelligent buyer to learn how to decide in an incomplete information multi-attribute bilateral simultaneous negotiation. The buyer does not know the negotiation strategy of the seller and only have access to the historical data of the previous negotiations. Using the historical data and clustering method, the type of seller is identified online during the negotiation. Then, the deep reinforcement learning method is utilized to support the buyer to learn its optimal decision. In the complete information case, we prove that the negotiation admits a unique Nash bargaining solution with possibly asymmetric negotiation powers. In comprehensive simulation studies, the efficiency of the proposed learning agent is evaluated in different scenarios and we show that the learning negotiation with incomplete information is converged to a Pareto optimal solution. Then, using the concept of the Nash bargaining solution, the negotiation power of the buyer is assessed in negotiation." @default.
• W3036684219 created "2020-06-25" @default.
• W3036684219 creator A5052397072 @default.
• W3036684219 creator A5081002970 @default.
• W3036684219 creator A5083876282 @default.
• W3036684219 date "2020-09-01" @default.
• W3036684219 modified "2023-10-04" @default.
• W3036684219 title "Learning pareto optimal solution of a multi-attribute bilateral negotiation using deep reinforcement" @default.
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• W3036684219 doi "https://doi.org/10.1016/j.elerap.2020.100987" @default.
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