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

Abstract

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.