Robot Emotion Response Model Based on Fuzzy Cognitive Map

HUANG Hongcheng; ZUO Rongrong; HU Min; TAO Yang; KOU Lan

doi:10.11999/JEIT210601

Volume 44 Issue 2

Feb. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(2): 486-495

HUANG Hongcheng, ZUO Rongrong, HU Min, TAO Yang, KOU Lan. Robot Emotion Response Model Based on Fuzzy Cognitive Map[J]. Journal of Electronics & Information Technology, 2022, 44(2): 486-495. doi: 10.11999/JEIT210601

Citation:

HUANG Hongcheng, ZUO Rongrong, HU Min, TAO Yang, KOU Lan. Robot Emotion Response Model Based on Fuzzy Cognitive Map[J]. Journal of Electronics & Information Technology, 2022, 44(2): 486-495. doi: 10.11999/JEIT210601

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Robot Emotion Response Model Based on Fuzzy Cognitive Map

doi: 10.11999/JEIT210601

HUANG Hongcheng^{1, 2
,
,},
ZUO Rongrong¹,
HU Min¹,
TAO Yang^{1, 2},
KOU Lan¹

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 Natural Science Foundation of China (61871062)

Received Date: 2021-06-18
Rev Recd Date: 2021-09-08

Available Online: 2021-09-25

Publish Date: 2022-02-25

Abstract

Abstract

In view of the existing human-computer interaction systems, which generally exist in the robot’s emotional response lack of uniqueness and initiative, and low participation degree, satisfaction degree and experience sense of participants, a model of emotional response of robots based on fuzzy cognitive map based on the emotional space of Pleasure-Arousal-Dominance (PAD) is proposed. Firstly, the participants’ interactive input emotion values are obtained, and the participants’ emotional state matrix is obtained by evaluating them. Secondly, considering the personality characteristics and social roles of the robot, the relationship between the current emotional state and the long-term emotional state of the context is established by using the time continuity of fuzzy cognitive map, so as to model the robot emotional response process. Finally, according to the influence of the robot’s personality and social role on the emotional response, the emotional state matrix of the robot is updated and mapped into the continuous emotional space to obtain the robot’s emotional response. The model comparison experiment results show that the proposed model can increase the initiative and uniqueness of the robot emotional response, and effectively improve the satisfaction of the participants, and increase the participants’ sense of experience.
- Robot,
- Human-computer interaction,
- Pleasure-Arousal-Dominance(PAD) emotion space,
- Fuzzy Cognitive Map(FCM),
- Emotional response

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References(22)

References

[1]	TAO Jianhua and TAN Tieniu. Affective computing: A review[C]. First International Conference on Affective Computing and Intelligent Interaction, Beijing, China, 2005: 981–995. doi: 10.1007/11573548_125.
[2]	ZHAO Zhi and HUANG Jinde. Emotion computing method based on knowledge representation[C]. 2020 International Conference on Computer Engineering and Application (ICCEA), Guangzhou, China, 2020: 368–372. doi: 10.1109/ICCEA50009.2020.00086.
[3]	韩晶, 解仑, 刘欣, 等. 基于Gross认知重评的机器人认知情感交互模型[J]. 东南大学学报:自然科学版, 2015, 45(2): 270–274. doi: 10.3969/j.issn.1001-0505.2015.02.014 HAN Jing, XIE Lun, LIU Xin, et al. Cognitive emotion interaction model of robot based on Gross cognitive reappraisal[J]. Journal of Southeast University:Natural Science Edition, 2015, 45(2): 270–274. doi: 10.3969/j.issn.1001-0505.2015.02.014
[4]	PARK S H, BAE B C, and CHEONG Y G. Emotion recognition from text stories using an emotion embedding model[C]. 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), Busan, Korea (South), 2020: 579–583,doi: 10.1109/BigComp48618.2020.00014.
[5]	孙晓, 彭晓琪, 胡敏, 等. 基于多维扩展特征与深度学习的微博短文本情感分析[J]. 电子与信息学报, 2017, 39(9): 2048–2055. doi: 10.11999/JEIT160975 SUN Xiao, PENG Xiaoqi, HU Min, et al. Extended multi-modality features and deep learning based microblog short text sentiment analysis[J]. Journal of Electronics &Information Technology, 2017, 39(9): 2048–2055. doi: 10.11999/JEIT160975
[6]	黄程韦, 赵艳, 金赟, 等. 实用语音情感的特征分析与识别的研究[J]. 电子与信息学报, 2011, 33(1): 112–116. doi: 10.3724/SP.J.1146.2009.00886 HUANG Chengwei, ZHAO Yan, JIN Yun, et al. A study on feature analysis and recognition of practical speech emotion[J]. Journal of Electronics &Information Technology, 2011, 33(1): 112–116. doi: 10.3724/SP.J.1146.2009.00886
[7]	丁永刚, 李石君, 付星, 等. 面向时序感知的多类别商品方面情感分析推荐模型[J]. 电子与信息学报, 2018, 40(6): 1453–1460. doi: 10.11999/JEIT170938 DING Yonggang, LI Shijun, FU Xing, et al. Temporal-aware multi-category products recommendation model based on aspect-level sentiment analysis[J]. Journal of Electronics &Information Technology, 2018, 40(6): 1453–1460. doi: 10.11999/JEIT170938
[8]	张国丽, 李祚泳. 基于蚁群算法的情感模型研究[J]. 计算机应用, 2009, 29(10): 2758–2761. doi: 10.3724/SP.J.1087.2009.02758 ZHANG Guoli and LI Zuoyong. Emotional model based on ant colony algorithm[J]. Journal of Computer Applications, 2009, 29(10): 2758–2761. doi: 10.3724/SP.J.1087.2009.02758
[9]	ZHANG Rui, WANG Zhenyu, and MAI Dongcheng. Building emotional conversation systems using multi-task Seq2Seq learning[C]. 6th CCF International Conference on Natural Language Processing and Chinese Computing, Dalian, China, 2018: 612–621. doi: 10.1007/978-3-319-73618-1_51.
[10]	FIRDAUS M, CHAUHAN H, EKBAL A, et al. EmoSen: Generating sentiment and emotion controlled responses in a multimodal dialogue system[J]. IEEE Transactions on Affective Computing, To be published. doi: 10.1109/TAFFC.2020.3015491.
[11]	SUN Xiao, LI Jia, WEI Xing, et al. Emotional editing constraint conversation content generation based on reinforcement learning[J]. Information Fusion, 2020, 56: 70–80. doi: 10.1016/j.inffus.2019.10.007
[12]	RUSSELL J A and MEHRABIAN A. Evidence for a three-factor theory of emotions[J]. Journal of Research in Personality, 1977, 11(3): 273–294. doi: 10.1016/0092-6566(77)90037-X
[13]	PARK J W, KIM W H, LEE W H, et al. How to completely use the PAD space for socially interactive robots[C]. 2011 IEEE International Conference on Robotics and Biomimetics, Karon Beach, Thailand, 2011: 3005–3010. doi: 10.1109/ROBIO.2011.6181762.
[14]	陶雪琼. 人工智能时代人机社会性交互设计研究[D]. [硕士论文], 江南大学, 2020. doi: 10.27169/d.cnki.gwqgu.2020.000441. TAO Xueqiong. Research on human-computer social interaction design in the age of artificial intelligence[D]. [Master dissertation], Jiangnan University, 2020. doi: 10.27169/d.cnki.gwqgu.2020.000441.
[15]	孟琦. 基于情感词强度极值的情感嵌入模型研究[D]. [硕士论文], 哈尔滨工程大学, 2019. MENG Qi. Research on sentiment embedding model based on the value of sentimental word intensity[D]. [Master dissertation], Harbin Institute of Technology, 2019.
[16]	GOLDBERG L R. An alternative "description of personality": The Big-Five factor structure[J]. Journal of Personality and Social Psychology, 1990, 59(6): 1216–1229. doi: 10.1037/0022-3514.59.6.1216
[17]	MEHRABIAN A. Analysis of the big-five personality factors in terms of the PAD temperament model[J]. Australian Journal of Psychology, 1996, 48(2): 86–92. doi: 10.1080/00049539608259510
[18]	PAPAGEORGIOU E I, STYLIOS C D, and GROUMPOS P P. Active Hebbian learning algorithm to train fuzzy cognitive maps[J]. International Journal of Approximate Reasoning, 2004, 37(3): 219–249. doi: 10.1016/j.ijar.2004.01.001
[19]	田智行, 陈夏铭, 姜大志. 一种离散状态与维度空间互映射的人工情感模型[J]. 系统仿真学报, 2021, 33(5): 1062–1069. doi: 10.16182/j.issn1004731x.joss.20-0007 TIAN Zhihang, CHEN Xiaming, and JIANG Dazhi. An artificial emotion model for the mutual mapping between discrete state and dimensional space[J]. Journal of System Simulation, 2021, 33(5): 1062–1069. doi: 10.16182/j.issn1004731x.joss.20-0007
[20]	GUNTHER C. ChatterBot tutorial[EB/OL]. https://chatterbot.readthedocs.io/en/stable/tutorial.html, 2021.
[21]	China Computer Federation. The 6th conference on natural language processing and Chinese computing Dalian[EB/OL]. http://tcci.ccf.org.cn/conference/2017/taskdata.php, 2017.
[22]	WU Yu, WU Wei, XING Chen, et al. Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots[C]. The 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017: 496–505. doi: 10.18653/v1/P17-1046.