Cognitive Emotion Interaction Model of Robot Based on Game Theory
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摘要: 针对现有的人机交互系统普遍存在情感缺失、参与人参与度不高的问题，该文依据PAD情感空间提出一种基于博弈的机器人认知情感交互模型。首先，对参与人的交互输入情感进行评估并分析当前人机交互关系，提取友好度和共鸣度2个影响因素。其次，模拟人际交往的心理博弈过程对参与人和机器人的情感生成过程进行建模，将嵌入博弈的子博弈完美均衡策略作为机器人的最优情感选择策略；最后，根据最优情感策略更新机器人的情感状态转移概率，并以6种基本情感的空间坐标为标签，得出受到情感刺激后机器人情感状态的空间坐标。实验结果表明，与其它认知交互模型相比，该文模型能够减少机器人对外界情感刺激的依赖并有效引导参与人参与人机交互，为机器人的情感认知建模提供了新的方法和思路。Abstract: To solve the problems of the existing in the process of human-computer interaction system, such as lack of emotion and low participation, a cognitive emotion interaction model based on game theory in PAD emotion space is proposed. Firstly, the interactive input emotion of participant is evaluated and some influence factors such as friendship and resonance are extracted to analyze the current human-computer interaction relationship. Secondly, modeling the emotional generation process of participants and robots by simulating the psychological game process in interpersonal communication, and the optimal emotional strategy of the robot is obtained by using the sub-game perfection equilibrium of the embedded game. Finally, the emotional state transition probability of the robot is updated according the optimal emotional strategy. The spatial coordinates of the six basic emotional states are used as labels to obtain the PAD spatial coordinate of the robot emotional state after emotional stimulate, The results of experiment show that compared with the others emotional interaction model, the proposed model can reduce the dependence of robots on external emotional stimuli and effective guide participants to participate in human-computer interaction, which provides some ideas for the emotion cognition model of robot in human-computer interaction.
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表 1 基于博弈的机器人认知情感交互模型构建
输入：$k{{ - 1}}$次会话后友好度更新值$F(k - 1)$和机器人的情感状态转移概率${{\text{P}}_{\rm{R}}}(k - 1)$, $k$次会话参与人的交互输入情感${\text{E}}_{{\rm{HR}}}^k$; 输出：$k + 1$次会话时机器人的情感值${\text{E}}_{{\rm{RH}}}^{k{{ + 1}}}$; Repeat: 参与人输入交互情感${\text{E}}_{{\rm{HR}}}^k$; 根据式(1)—式(3)将${\text{E}}_{{\rm{HR}}}^k$评估转化为强度值向量${\text{P}}({\text{E}}_{{\rm{HR}}}^k)$； 根据式(8)—式(11)计算针对$k + 1$次会话机器人每种情感策略选择，预测$k + 2$次会话参与人每种情感策略选择，$k + 3$次会话机器人每种情
感策略下参与人和机器人的效用值；根据式(12)，式(13)求解机器人的情感选择策略$s$； 通过最优情感策略$s$对机器人的情感状态转移概率进行更新，对机器人情感的空间坐标进行标定； 更新人机交互友好度，并令$k = k + 2$； Until 参与人停止输入交互情感； 人机交互会话结束。 
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表 2 不同认知模型的自动评价结果
模型 MRR MAP Seq2Seq 0.3836 0.4015 ChatterBot 0.4623 0.4923 MECs 0.5903 0.6091 GCRs 0.6269 0.6435 本文 0.6507 0.6756
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表 3 参与人与不同认知模型作用下的机器人交互的次数与时间统计
机器人的认知模型 平均交互轮数(轮) 平均交互时间(s) Seq2Seq 9 98.32 ChatterBot 6 60.69 MECs 7 88.16 GCRs 10 110.38 本文 12 130.51
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