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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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References

  1. Baarslag, T., et al.: Evaluating practical negotiating agents: results and analysis of the 2011 international competition. Artif. Intell. J. (AIJ) 198, 73–103 (2013)

    Article  Google Scholar 

  2. Baarslag, T., Hindriks, K.V.: Accepting optimally in automated negotiation with incomplete information. In: Proceedings of the 12th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2013, pp. 715–722 (2013)

    Google Scholar 

  3. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)

  4. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

  5. Fujita, K.: Compromising adjustment strategy based on TKI conflict mode for multi-times bilateral closed negotiations. Comput. Intell. 34, 85–103 (2017). https://doi.org/10.1111/coin.12107

    Article  MathSciNet  Google Scholar 

  6. Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Wooldridge, M.J., Sierra, C.: Automated negotiation: prospects, methods and challenges. Group Decis. Negot. 10(2), 199–215 (2001)

    Article  Google Scholar 

  7. Jonge, D.D., Zhang, D.: Automated negotiations for general game playing. In: Proceedings of the 16th Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2017, pp. 371–379 (2017)

    Google Scholar 

  8. Jonker, C.M., Aydogan, R., Baarslag, T., Fujita, K., Ito, T., Hindriks, K.V.: Automated negotiating agents competition (ANAC). In: AAAI, pp. 5070–5072 (2017)

    Google Scholar 

  9. Kalai, E.: Proportional solutions to bargaining situations: interpersonal utility comparisons. Discussion Papers 179. Center for Mathematical Studies in Economics and Management Science, Northwestern University, March 1977. https://ideas.repec.org/p/nwu/cmsems/179.html

  10. Kalai, E., Smorodinsky, M.: Other solutions to Nash’s bargaining problem. Econometrica 43(3), 513–518 (1975). http://www.jstor.org/stable/1914280

    Article  MathSciNet  Google Scholar 

  11. Kawaguchi, S., Fujita, K., Ito, T.: Compromising strategy based on estimated maximum utility for automated negotiation agents competition (ANAC-10). In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds.) IEA/AIE 2011. LNCS, vol. 6704, pp. 501–510. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21827-9_51

    Chapter  Google Scholar 

  12. Lewis, M., Yarats, D., Dauphin, Y.N., Parikh, D., Batra, D.: Deal or no deal? End-to-end learning for negotiation dialogues. CoRR abs/1706.05125 (2017). http://arxiv.org/abs/1706.05125

  13. Lin, R., Kraus, S., Baarslag, T., Tykhonov, D., Hindriks, K., Jonker, C.M.: Genius: an integrated environment for supporting the design of generic automated negotiators. Comput. Intell. 30, 48–70 (2012). https://doi.org/10.1111/j.1467-8640.2012.00463.x

    Article  MathSciNet  Google Scholar 

  14. Mell, J., Gratch, J.: Grumpy & Pinocchio: answering human-agent negotiation questions through realistic agent design. In: Proceedings of the 16th Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2017, pp. 401–409 (2017)

    Google Scholar 

  15. Nash, J.F., et al.: Equilibrium points in n-person games. Proc. Nat. Acad. Sci. 36(1), 48–49 (1950)

    Article  MathSciNet  Google Scholar 

  16. Nash Jr., J.F.: The bargaining problem. Econometrica: J. Econ. Soc. 155–162 (1950)

    Article  MathSciNet  Google Scholar 

  17. Shinohara, H., Fujita, K.: Alternating offers protocol considering fair privacy for multilateral closed negotiation. In: An, B., Bazzan, A., Leite, J., Villata, S., van der Torre, L. (eds.) PRIMA 2017. LNCS, vol. 10621, pp. 533–541. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69131-2_36

    Chapter  Google Scholar 

  18. Sordoni, A., et al.: A neural network approach to context-sensitive generation of conversational responses. In: Proceedings of the 2015 Conference of the NAACL-HLT, pp. 196–205 (2015)

    Google Scholar 

  19. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  20. Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1422–1432 (2015)

    Google Scholar 

  21. Tieleman, T., Hinton, G.: Lecture 6.5—RmsProp: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Netw. Mach. Learn. 4, 26–31 (2012)

    Google Scholar 

  22. Zafari, F., Mofakham, F.N.: POPPONENT: highly accurate, individually and socially efficient opponent preference model in bilateral multi issue negotiations. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 5100–5104 (2017)

    Google Scholar 

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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

  • eBook Packages: Computer ScienceComputer Science (R0)

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