Prediction of Nash Bargaining Solution in Negotiation Dialogue

  • Kosui IwasaEmail author
  • Katsuhide FujitaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11012)


There are several studies that focus on the support systems that can be used for human-human negotiations. However, existing automated agent-agent negotiation systems require the participants to set utility functions manually. Additionally, a method to predict the utility functions from the negotiation dialogues in natural language and to find a bargaining solution has not been proposed yet. By developing such a method, the existing research related to automated negotiations can be utilized for the negotiation dialogues in real-life situations. Therefore, we propose a method to predict the utility function of each agent and Nash bargaining solution only from the negotiation dialogues using gated recurrent units (GRUs) [4] with attention [3]. We demonstrate that the rate of Nash bargaining solution that was obtained by using our method outperforms the rate that was obtained while humans were negotiating.


Automated negotiation Natural language processing Recurrent neural networks 



This work was supported by JST CREST Grant Number JPMJCR15E1, Japan.


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Authors and Affiliations

  1. 1.Graduate School of EngineeringTokyo University of Agriculture and TechnologyTokyoJapan
  2. 2.Institute of EngineeringTokyo University of Agriculture and TechnologyTokyoJapan

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