Advertisement

Prediction of Nash Bargaining Solution in Negotiation Dialogue

  • Kosui IwasaEmail author
  • Katsuhide FujitaEmail author
Conference paper
  • 2k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11012)

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.

Keywords

Automated negotiation Natural language processing Recurrent neural networks 

Notes

Acknowledgements

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

References

  1. 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)CrossRefGoogle Scholar
  2. 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. 3.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
  4. 4.
    Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
  5. 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.12107MathSciNetCrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 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. 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. 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. 10.
    Kalai, E., Smorodinsky, M.: Other solutions to Nash’s bargaining problem. Econometrica 43(3), 513–518 (1975). http://www.jstor.org/stable/1914280MathSciNetCrossRefGoogle Scholar
  11. 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_51CrossRefGoogle Scholar
  12. 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. 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.xMathSciNetCrossRefGoogle Scholar
  14. 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. 15.
    Nash, J.F., et al.: Equilibrium points in n-person games. Proc. Nat. Acad. Sci. 36(1), 48–49 (1950)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Nash Jr., J.F.: The bargaining problem. Econometrica: J. Econ. Soc. 155–162 (1950)MathSciNetCrossRefGoogle Scholar
  17. 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_36CrossRefGoogle Scholar
  18. 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. 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)MathSciNetzbMATHGoogle Scholar
  20. 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. 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. 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

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

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

Personalised recommendations