Title of article :
A reinforcement learning optimized negotiation method based on mediator agent
Author/Authors :
Chen، نويسنده , , Lihong and Dong، نويسنده , , Hongbin and Zhou، نويسنده , , Yang، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2014
Abstract :
This paper firstly proposes a bilateral optimized negotiation model based on reinforcement learning. This model negotiates on the issue price and the quantity, introducing a mediator agent as the mediation mechanism, and uses the improved reinforcement learning negotiation strategy to produce the optimal proposal. In order to further improve the performance of negotiation, this paper then proposes a negotiation method based on the adaptive learning of mediator agent. The simulation results show that the proposed negotiation methods make the efficiency and the performance of the negotiation get improved.
Keywords :
Multi-agent system , Mediator agent , Optimized negotiation , Negotiation strategy , reinforcement learning , adaptive learning
Journal title :
Expert Systems with Applications
Journal title :
Expert Systems with Applications