MCTS-based Automated Negotiation Agent

TL;DR

This paper presents a novel automated negotiation agent that uses Monte Carlo Tree Search combined with opponent modeling techniques, outperforming existing agents in multi-dimensional, time-unpressured negotiations.

Contribution

Introduces a new negotiation agent leveraging MCTS and Bayesian opponent modeling, with demonstrated superior performance over existing strategies.

Findings

Our agent outperforms Random Walker, Tit-for-tat, and Nice Tit-for-Tat.

The approach is modular and adaptable for specific applications.

The agent effectively handles multi-dimensional negotiations in continuous and discrete domains.

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.