  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.
