[1F5-GS-5] Agents

[1F5-GS-5-01] Strategy Selection using Clustering for Meta-Strategy in Automated Negotiation

〇Hiroyasu Yoshino¹, Katsuhide Fujita¹ (1. Tokyo University of Agriculture and Technology)

Keywords:Automated Negotiation, Multi-Agent System, Reinforcement Learning

This paper aims to develop an automated negotiation meta-strategy and proposes an approach that automatically selects a strategy according to the opponent from a set of multiple strategies using clustering. The proposed method makes groups of the strategies of possible negotiation opponents. It learns an effective bidding strategy corresponding to the representative point of each cluster using deep reinforcement learning, which is for the average agent in each cluster and is strong on average against the agents in the cluster. We analyzed the number of clusters of the strategies retained by the proposed method and found that the individual utility tends to be higher when the number of clusters is small, and especially the utility was highest when the number of clusters is 3. In addition, the negotiation simulation experiments demonstrated that the proposed method gained higher individual utility than the previous study.