CAJ | 학술논문

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。
건립일충기우정서적Nash-Q결책모형，타유인지층화정서층조성。인지층모형유Nash-Q산법실현，정서층건립재정서기억화평개이론지상，유고흥、상심、공구、염번조성정서공간，건립상응자격여정서영사모형、정서여행위동작영사모형、매충정서하적동작신임도평개모형。장문중모형응용도량지능체망격결책실험중，결과표명정서층적인입가가쾌수렴속도，동시능유효방지함입국부최우，경호겸고재선학습적“보수”화“탐색”평형。
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.