【摘要】 在基于效用的行为选择模型基础上对多Agent系统中个性建模问题进行研究。利用人工神经网络能够学习到人类难以理解的目标函数的特点,结合心理学中个性的五因素模型建立Agent个性神经网络,通过不同参数反映个性对效用变化的影响方式,具有更强的个性表征能力。设计梯度下降的学习算法训练Agent相应的个性神经网络。实验验证了该模型刻画Agent个性的有效性。更多还原

【Abstract】 Based on utility-based action selection model,this paper researches on personality modeling in multi-Agent system.That artificial neural network can learn target function which is hard to comprehend draws attention.So an individualized neural network is built according to five-factor model in psychology.Different setting on parameters reflects the way that personality influences utility of actions.This model has stronger ability to describe personality.A gradient descending learning algorithm is proposed to train the individualized neural network.The model is validated on personality exhibition during action selection.更多还原