MCTS based Automated Negotiation Agent
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 methods since it has been used with success on games with high branching factor such as Go. It uses two opponent modeling techniques that are used by 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 ours; however 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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