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Prediction of Nash Bargaining Solution in Negotiation Dialogue

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PRICAI 2018: Trends in Artificial Intelligence (PRICAI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11012))

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Abstract

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.

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Acknowledgements

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

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Authors and Affiliations

  1. Graduate School of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan

    Kosui Iwasa

  2. Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan

    Katsuhide Fujita

Authors
  1. Kosui Iwasa
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  2. Katsuhide Fujita
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Corresponding authors

Correspondence to Kosui Iwasa or Katsuhide Fujita .

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Editors and Affiliations

  1. Southeast University, Nanjing, China

    Xin Geng

  2. University of Tasmania, Hobart, Tasmania, Australia

    Byeong-Ho Kang

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Iwasa, K., Fujita, K. (2018). Prediction of Nash Bargaining Solution in Negotiation Dialogue. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_60

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  • DOI: https://doi.org/10.1007/978-3-319-97304-3_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97303-6

  • Online ISBN: 978-3-319-97304-3

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