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

TL;DR

MoCaNA is an automated negotiation agent that employs Monte Carlo Tree Search for bidding, capable of negotiating in continuous and unbounded domains, outperforming baseline and some state-of-the-art agents.

Contribution

This paper introduces MoCaNA, a novel negotiation agent using Monte Carlo Tree Search with opponent modeling for continuous and unbounded negotiation domains.

Findings

MoCaNA outperforms RandomWalker in unbounded domains.

MoCaNA beats most ANAC 2014 finalists in bounded domains.

The agent effectively models opponents' strategies and utilities.

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.