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MCTS-based Automated Negotiation Agent

Abstract : This paper introduces a new negotiating agent model for automated negotiation. We focus on applications without time pressure with multidi-mensional 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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https://hal.archives-ouvertes.fr/hal-02282222
Contributor : Cédric Buron <>
Submitted on : Thursday, September 12, 2019 - 8:29:18 AM
Last modification on : Saturday, October 24, 2020 - 11:18:02 PM
Long-term archiving on: : Friday, February 7, 2020 - 10:17:24 PM

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Cédric Buron, Zahia Guessoum, Sylvain Ductor. MCTS-based Automated Negotiation Agent. The 22nd International Conference on Principles and Practice of Multi-Agent Systems (PRIMA2019), Oct 2019, Torino, Italy. pp.186-201, ⟨10.1007/978-3-030-33792-6_12⟩. ⟨hal-02282222⟩

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