Abstract An emotion decision-making model consisting of cognition layer and emotion layer is constructed, the cognition layer is implemented in the Nash-Q algorithm, and the emotion layer is based on the theory of emotion memory and evaluation. The emotion space includes happiness, sadness, fear, boredom. The stimulus-to-emotion mapping model, emotion-to-action mapping model and the evaluation model of action credibility for each emotion are built respectively. The proposed model is applied to two-agent grid decision-making experiment. The results show that the convergence speed is higher when the Nash-Q algorithm is combined with emotional layer, and the model can effectively avoid local optimum. The model keeps better balance between conservation and searching in online learning.
2015, Vol. 28 
