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MCTS-Based Automated Negotiation Agent

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PRIMA 2019: Principles and Practice of Multi-Agent Systems (PRIMA 2019)

Part of the book series: Lecture Notes in Computer Science ((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.

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Notes

  1. 1.

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

  2. 2.

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

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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
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  2. Zahia Guessoum
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  3. Sylvain Ductor
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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

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

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33791-9

  • Online ISBN: 978-3-030-33792-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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