  Automated negotiation is a rising topic in Artificial Intelligence research.
Monte Carlo methods have got increasing interest, in particular since they have
been used with success on games with high branching factor such as go.In this
paper, we describe an Monte Carlo Negotiating Agent (MoCaNA) whose bidding
strategy relies on Monte Carlo Tree Search. We provide our agent with opponent
modeling tehcniques for bidding strtaegy and utility. MoCaNA can negotiate on
continuous negotiating domains and in a context where no bound has been
specified. We confront MoCaNA and the finalists of ANAC 2014 and a RandomWalker
on different negotiation domains. Our agent ouperforms the RandomWalker in a
domain without bound and the majority of the ANAC finalists in a domain with a
bound.
