Skip to main content
Book cover

International Conference on Principles and Practice of Multi-Agent Systems

PRIMA 2019: PRIMA 2019: Principles and Practice of Multi-Agent Systems pp 186–201Cite as

MCTS-Based Automated Negotiation Agent

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11873)

Abstract

This paper introduces a new negotiating agent model for automated negotiation. We focus on applications without time pressure with multidimensional negotiation on both continuous and discrete domains. The agent bidding strategy relies on Monte Carlo Tree Search, which is a trendy method since it has been used with success on games with high branching factor such as Go. It also exploits opponent modeling techniques thanks to Gaussian process regression and Bayesian learning. Evaluation is done by confronting the existing agents that are able to negotiate in such context: Random Walker, Tit-for-tat and Nice Tit-for-Tat. None of those agents succeeds in beating our agent. Also, the modular and adaptive nature of our approach is a huge advantage when it comes to optimize it in specific applicative contexts.

Keywords

  • Automated negotiation
  • MCTS
  • Supply chain

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-33792-6_12
  • Chapter length: 16 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-33792-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   139.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.

Notes

  1. 1.

    http://math.nist.gov/javanumerics/.

  2. 2.

    http://commons.apache.org/proper/commons-math/.

References

  1. Baarslag, T.: Exploring the Strategy Space of Negotiating Agents: A Framework for Bidding, Learning and Accepting in Automated Negotiation. Ph.D. thesis, Delft University of Technology (2016)

    Google Scholar 

  2. Baarslag, T., Hendrikx, M.J.C., Hindriks, K.V., Jonker, C.M.: Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques. Auton. Agents Multi-Agent Syst. 20(1), 1–50 (2015). https://doi.org/10.1007/s10458-015-9309-1

    CrossRef  Google Scholar 

  3. Baarslag, T., Hindriks, K.V.: Accepting optimally in automated negotiation with incomplete information. In: AAMAS 2013, pp. 715–722. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2013). http://dl.acm.org/citation.cfm?id=2484920.2485033

  4. Baarslag, T., Hindriks, K.V., Jonker, C.: A tit for tat negotiation strategy for real-time bilateral negotiation. In: Ito, T., Zhang, M., Robu, V., Matsuo, T. (eds.) Complex Automated Negotiations: Theories, Models, and Software Competitions, vol. 435, pp. 229–233. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-30737-9_18

    CrossRef  Google Scholar 

  5. Browne, C.C., et al.: A survey of Monte Carlo tree search methods. IEEE Trans. Comput. Intell. AI Games 4(1), 1–43 (2012). https://doi.org/10.1109/TCIAIG.2012.2186810

    CrossRef  Google Scholar 

  6. Coulom, R.: Efficient selectivity and backup operators in Monte-Carlo tree search. In: van den Herik, H.J., Ciancarini, P., Donkers, H.H.L.M.J. (eds.) CG 2006. LNCS, vol. 4630, pp. 72–83. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75538-8_7

    CrossRef  Google Scholar 

  7. Couëtoux, A.: Monte Carlo Tree Search for Continuous and Stochastic Sequential Decision Making Problems. Ph.D. thesis, Université Paris XI (2013)

    Google Scholar 

  8. Dziuba, D.T.: Crowdfunding platforms in invoice trading as alternative financial markets. Roczniki Kolegium Analiz Ekonomicznych/Szkoła Główna Handlowa 49, 455–464 (2018)

    Google Scholar 

  9. Fang, F., Xin, Y., Xia, Y., Haitao, X.: An opponent’s negotiation behavior model to facilitate buyer-seller negotiations in supply chain management. In: 2008 International Symposium on Electronic Commerce and Security (2008). https://doi.org/10.1109/ISECS.2008.93

  10. Faratin, P., Jennings, N.R., Sierra, C.: Negotiation decision functions for autonomous agents. Robot. Auton. Syst. 24(3–4), 159–182 (1998). https://doi.org/10.1016/S0921-8890(98)00029-3

    CrossRef  Google Scholar 

  11. Finnsson, H.: Generalized Monte Carlo tree search extensions for general game playing. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2012, pp. 1550–1556. AAAI Press (2012). http://dl.acm.org/citation.cfm?id=2900929.2900948

  12. Fukuta, N., Ito, T., Zhang, M., Fujita, K., Robu, V. (eds.): Recent Advances in Agent-based Complex Automated Negotiation. SCI, vol. 638. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30307-9

    CrossRef  Google Scholar 

  13. Guttman, R.H., Moukas, A.G., Maes, P.: Agent-mediated electronic commerce: a survey. Knowl. Eng. Rev. 13(02), 147–159 (1998). https://doi.org/10.1007/3-540-58266-5_1. http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=71015&fileId=S0269888998002082

  14. Hindriks, K., Tykhonov, D.: Opponent modelling in automated multi-issue negotiation using bayesian learning. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 331–338 (2008). http://dl.acm.org/citation.cfm?id=1402383.1402433

  15. de Jonge, D., Sierra, C.: GANGSTER: an automated negotiator applying genetic algorithms. In: Fukuta, N., Ito, T., Zhang, M., Fujita, K., Robu, V. (eds.) Recent Advances in Agent-based Complex Automated Negotiation. SCI, vol. 638, pp. 225–234. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30307-9_14

    CrossRef  Google Scholar 

  16. de Jonge, D., Zhang, D.: Automated negotiations for general game playing. In: AAMAS 2017, Richland, SC, pp. 371–379 (2017). http://dl.acm.org/citation.cfm?id=3091125.3091183

  17. Kocsis, L., Szepesvári, C.: Bandit based Monte-Carlo planning. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 282–293. Springer, Heidelberg (2006). https://doi.org/10.1007/11871842_29

    CrossRef  Google Scholar 

  18. 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(1), 48–70 (2014). https://doi.org/10.1111/j.1467-8640.2012.00463.x

    CrossRef  MathSciNet  Google Scholar 

  19. Nash Jr., J.F.: The bargaining problem. Econometrica J. Econ. Soc. 18, 155–162 (1950)

    CrossRef  MathSciNet  Google Scholar 

  20. Osborne, M.J., Rubinstein, A.: A Course in Game Theory, 12th edn. MIT Press, Cambridge (1994)

    MATH  Google Scholar 

  21. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006)

    MATH  Google Scholar 

  22. Rubinstein, A.: Perfect equilibrium in a bargaining model. Econometrica J. Econ. Soc. 50, 97–109 (1982)

    CrossRef  MathSciNet  Google Scholar 

  23. Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)

    CrossRef  Google Scholar 

  24. Williams, C.R., Robu, V., Gerding, E.H., Jennings, N.R.: Using Gaussian processes to optimise concession in complex negotiations against unknown opponents. In: IJCAI 2011, pp. 432–438 (2011). https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-080

Download references

Author information

Authors and Affiliations

  1. Thales Research and Technology, 1 av. Augustin Fresnel, Palaiseau, France

    Cédric L. R. Buron

  2. Lip6, Sorbonne Université, Paris, France

    Zahia Guessoum

  3. CReSTIC, Université de Reims Champagne Ardennes, Reims, France

    Zahia Guessoum

  4. Universidade Estadual do Ceará, Fortaleza, Brazil

    Sylvain Ductor

Authors
  1. Cédric L. R. Buron
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zahia Guessoum
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sylvain Ductor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cédric L. R. Buron .

Editor information

Editors and Affiliations

  1. Università degli Studi di Torino, Turin, Italy

    Matteo Baldoni

  2. Utrecht University, Utrecht, The Netherlands

    Mehdi Dastani

  3. Zhejiang University, Hangzhou, China

    Beishui Liao

  4. National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

    Yuko Sakurai

  5. University of Texas at Dallas, Richardson, TX, USA

    Rym Zalila Wenkstern

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Buron, C.L.R., Guessoum, Z., Ductor, S. (2019). MCTS-Based Automated Negotiation Agent. In: Baldoni, M., Dastani, M., Liao, B., Sakurai, Y., Zalila Wenkstern, R. (eds) PRIMA 2019: Principles and Practice of Multi-Agent Systems. PRIMA 2019. Lecture Notes in Computer Science(), vol 11873. Springer, Cham. https://doi.org/10.1007/978-3-030-33792-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33792-6_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33791-9

  • Online ISBN: 978-3-030-33792-6

  • eBook Packages: Computer ScienceComputer Science (R0)