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

MoCaNA, un agent de n{\'e}gociation automatique utilisant la recherche arborescente de Monte-Carlo