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MCTS-Based Automated Negotiation Agent

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


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.


  • Automated negotiation
  • MCTS
  • Supply chain

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  • DOI: 10.1007/978-3-030-33792-6_12
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  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).

    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).

  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).

    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).

    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).

    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).

  10. Faratin, P., Jennings, N.R., Sierra, C.: Negotiation decision functions for autonomous agents. Robot. Auton. Syst. 24(3–4), 159–182 (1998).

    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).

  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).

    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).

  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).

  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).

    CrossRef  Google Scholar 

  16. de Jonge, D., Zhang, D.: Automated negotiations for general game playing. In: AAMAS 2017, Richland, SC, pp. 371–379 (2017).

  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).

    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).

    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).

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Correspondence to Cédric L. R. Buron .

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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.

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