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MCTS-based Automated Negotiation Agent

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Cédric Buron, Zahia Guessoum (CRESTIC), Sylvain Ductor (UECE)

Multiagent Systems · Submitted 12 Sep 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim None of those agents succeeds in beating our agent.

how it is stated · asserted 1.00 read the record →

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arXiv ID1909.09461 (version 1)
Pith ID7TOQL4HR, Pith's permanent record of this paper and its review
CitationC\'edric Buron, Sylvain Ductor (UECE), Zahia Guessoum (CRESTIC). "MCTS-based Automated Negotiation Agent." arXiv:1909.09461 (2019). https://pith.science/paper/7TOQL4HR
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,390 input tokens.

What the abstract itself states, as probabilities: claims first 0.12 · states limitation 0.29 · human participants 0.03 · controlled comparison 0.79 · randomized assignment 0.02 · states quantitative result 0.06 · compares with prior results 0.97 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This paper introduces a new negotiating agent model for automated negotiation. contribution 1.00 0.87 0.06 asserted 1.00
s1 We focus on applications without time pressure with multidi-mensional negotiation on both continuous and discrete domains. background 0.46 0.31 0.02 asserted 0.96
s2 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. method 0.96 0.34 0.04 asserted 1.00
s3 It also exploits opponent modeling techniques thanks to Gaussian process regression and Bayesian learning. method 0.98 0.76 0.03 asserted 1.00
s4 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. method 0.97 0.89 0.04 asserted 0.97
s5 None of those agents succeeds in beating our agent. result 1.00 0.90 0.54 asserted 1.00
s6 Also, the modular and adaptive nature of our approach is a huge advantage when it comes to optimize it in specific applicative contexts. interpretation 0.92 0.35 0.13 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.63.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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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How each label was checked · JSON

Found among the papers closest to this one by title and main claim. Pith's classifier compared each pair's main results and a second model family confirmed the relation; checked against two further families, shown conflicts agreed 118 times in 120 and shown same results 218 times in 239. The quoted sentence is the other paper's main claim.

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