  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.
