MCTS-based Automated Negotiation Agent (Extended Abstract)
This work addresses negotiation automation for applications requiring flexible, adaptive strategies, though it appears incremental by combining existing methods like MCTS with opponent modeling.
The paper tackles automated negotiation in continuous domains without deadlines by introducing a Monte Carlo Tree Search-based agent with opponent modeling, which outperformed existing agents like Random Walker and Tit-for-tat in evaluations.
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.