# MCTS-based Automated Negotiation Agent (Extended Abstract)

## TL;DR

This paper presents a novel Monte Carlo Tree Search-based negotiation agent for continuous domains without deadlines, utilizing opponent modeling techniques to outperform existing agents in automated negotiation scenarios.

## Contribution

Introduces a new negotiation agent leveraging MCTS and opponent modeling, offering improved performance and adaptability over existing methods.

## Key findings

- Our agent outperforms Random Walker, Tit-for-tat, and Nice Tit-for-Tat.
- The MCTS-based approach is effective in continuous negotiation domains.
- Modular design allows for easy optimization in specific contexts.

## Abstract

This paper introduces a new Negotiating Agent for automated negotiation on continuous domains and without considering a specified deadline. 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 uses two opponent modeling techniques for its bidding strategy and its utility: 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; moreover the modular and adaptive nature of our approach is a huge advantage when it comes to optimize it in specific applicative contexts.