Reinforcement Q-Learning and Neural Networks to Acquire Negotiation Behaviors

Abstract

Learning in negotiation is fundamental for understanding human behaviors as well as for developing new solution concepts. Elsewhere, negotiation behaviors, in which the characters such as Conciliatory (Con), Neutral (Neu), or Aggressive (Agg) define a ‘psychological’ aspect of the negotiator personality, play an important role. In this paper, first, a brief description of SISINE (Integrated System of Simulation for Negotiation) project, which aims to develop innovative teaching methodology of negotiation skills, is given. Second, a negotiation approach essentially based on the escalation level and negotiator personality is suggested for SISINE. In fact, the escalation level defines gradually different negotiation stages from agreement to interruption. Afterwards, negotiation behaviors acquired by reinforcement Q-learning and Neural Networks (NN) under supervised learning are developed. Then, behavior results which display the suggested approach ability to provide negotiators with a first intelligence level are presented. Finally, a discussion is given to evaluate this first intelligence level.