An Automated Negotiation Agent Based on Shared and Local Issues

抄録

In the field of automated negotiation, there has been a growing interest in models that can explain the rational decisions of automated negotiating agents in order to gain the trust of users. Those models enable humans to trust agents by understanding their behavioral principles. In specific, in automated negotiation, appropriate compromises need to be made during the negotiation to match the other negotiating party in order to reach an agreement that is mutually beneficial. However, the negotiating agents currently use simple negotiation models. In this paper, we propose an automated negotiation model based on Q-learning. This enables the negotiating agent to make appropriate compromises to match the other negotiating party, which results in greater mutual benefit. The experimental evaluations show that the proposed agent is faster and has better results than the existing agents.