Bilateral multi-issue parallel negotiation model based on reinforcement learning

Abstract

This paper proposes a bilateral multi-issue parallel negotiation model based on reinforcement learning. Considering the equality of both sides and that both negotiators refuse to give more information for their own interests, it introduces a mediator agent as the mediation mechanism. It considers the correlation between quantity and price simultaneously, and uses reinforcement learning to generate the optimal behavioral strategy. Comparing with 'A simultaneous multi-issue negotiation through autonomous agents', the experimental results show that the proposed method has decreased little in the negotiation joint utility, but has decreased significantly in the negotiation times , and has also improved the equality of both negotiators. © 2013 Springer-Verlag.