黄宏程^{1, 2},
李净¹,
胡敏¹,
陶洋^{1, 2,,},
寇兰¹
1.重庆邮电大学通信与信息工程学院 重庆 400065
2.重庆市通信软件工程技术研究中心 重庆 400065
基金项目:国家重点研发计划(2019YFB2102001)，国家自然科学基金(61871062)

详细信息

作者简介:黄宏程：男，1979年生，副教授，研究方向为认知情感计算、复杂网络与信息传播理论
李净：女，1995年生，硕士生，研究方向为认知情感计算
胡敏：女，1971年生，副教授，研究方向为信息通信网络体系结构、人机交互理论与技术应用
陶洋：男，1964年生，教授，研究方向为人工智能、大数据与计算智能
寇兰：女，1963年生，副教授，研究方向为D2D通信、人机交互理论与技术应用

通讯作者:陶洋　taoyang@cqupt.edu.cn

中图分类号:TP242.6

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出版历程

收稿日期:2019-12-24
修回日期:2021-02-22
网络出版日期:2021-03-17
刊出日期:2021-06-18

Cognitive Emotional Interaction Model of Robot Based on Reinforcement Learning

Hongcheng HUANG^{1, 2},
Jing LI¹,
Min HU¹,
Yang TAO^{1, 2,,},
Lan KOU¹
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Chongqing Engineering Research Center of Communication Software, Chongqing 400065, China
Funds:The National Key Research and Development Project (2019YFB2102001), The National Natural Science Foundation of China (61871062)


摘要
摘要:为增强机器人的认知情感计算能力，依据PAD情感空间建立结合即时反馈和长期趋势的机器人认知情感生成方法，该文提出一种基于强化学习的机器人认知情感交互模型。首先，依据人际交往心理学理论，模拟人类情感生成过程进行类人情感生成，并从中提取相似性、积极性、共情性3个影响因素；其次，利用强化学习的全局统筹特性，建立响应情感状态与上下文长期情感状态之间的关联关系，从而对机器人情感生成过程进行建模；然后，将3个因素纳入模型奖励机制用于交互情感状态评估，实现模型更新并得到最优情感策略；最后，利用所得最优情感策略对应的最优情感状态对机器人情感状态转移概率进行更新，并依据6种基本情感状态在空间中的情感值，将其映射到连续情感空间中得到机器人的最优响应情感值。主客观对比实验表明，该文模型能有效增加机器人情感表达的细腻性、连续性、积极性以及共情性，还能有效降低机器人对外界情感刺激的依赖性，进一步提升和谐友好的人机交互关系。
关键词:PAD情感空间/
强化学习/
情感状态转移/
认知情感生成
Abstract:In order to enhance the cognitive emotional computing ability of robot, a cognitive emotional interaction model of robot based on reinforcement learning is proposed, which combines immediate feedback and long-term trend according to PAD(Pleasure-Arousal-Dominance) emotional space. Firstly, according to the psychology theory of interpersonal communication, the human emotion generation process is simulated to generate human-like emotions, and the three influencing factors of similarity, positivity and empathy are extracted. Secondly, the relationship between the response emotion+ state and the contexted long-term emotion state is established by using the global co-ordination feature of reinforcement learning, so as to model the robot emotion generation process. Then, three factors are incorporated into the model reward mechanism for the evaluate of the interactive emotion state, to update the model and get the optimal emotional strategy. Finally, the optimal emotional state corresponding to the obtained optimal emotional strategy is used to update the robot's emotional state transition probability, and based on the sentiment values of the six basic emotional states in space, them are mapped to continuous emotional space to get the optimal response emotional value of the robot. Subjective and objective comparison experiments show that the model in this paper can effectively increase the delicateness, continuity, positivity and empathy of the robot's emotional expression, and can effectively reduce the robot's dependence on external emotional stimuli, further improving the harmonious and friendly human-computer interaction.
Key words:Pleasure-Arousal-Dominance (PAD) emotion space/
Reinforcement learning/
Emotional state transfer/
Cognitive emotion generation



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