多智能体环境下的情绪决策模型

An Emotion Decision-Making Model in Multi-agent Environment

建立一种基于情绪的Nash-Q决策模型,它由认知层和情绪层组成.认知层模型由Nash-Q算法实现,情绪层建立在情绪记忆和评价理论之上,由高兴、伤心、恐惧、厌烦组成情绪空间,建立相应刺激与情绪映射模型、情绪与行为动作映射模型、每种情绪下的动作信任度评价模型.将文中模型应用到两智能体网格决策实验中,结果表明情绪层的引入可加快收敛速度,同时能有效防止陷入局部最优,更好兼顾在线学习的"保守"和"探索"平衡.

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.