MCTS-based Automated Negotiation Agent
This work addresses automated negotiation for applications requiring adaptive strategies, but it appears incremental as it combines existing techniques (MCTS and opponent modeling) in a new context.
The paper tackles automated negotiation in multi-dimensional domains without time pressure by developing an agent using Monte Carlo Tree Search with opponent modeling via Gaussian process regression and Bayesian learning. The agent outperformed existing baseline agents (Random Walker, Tit-for-tat, Nice Tit-for-Tat) in evaluations, though no specific numerical results are provided.
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.