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Volume 41 Issue 10
Oct.  2019
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Hongcheng HUANG, Ning LIU, Min HU, Yang TAO, Lan KOU. Cognitive Emotion Interaction Model of Robot Based on Game Theory[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2471-2478. doi: 10.11999/JEIT180867
Citation: Hongcheng HUANG, Ning LIU, Min HU, Yang TAO, Lan KOU. Cognitive Emotion Interaction Model of Robot Based on Game Theory[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2471-2478. doi: 10.11999/JEIT180867

Cognitive Emotion Interaction Model of Robot Based on Game Theory

doi: 10.11999/JEIT180867
Funds:  The National Natural Science Foundation of China (61871062), The Scientific Research Foundation of Chongqing University of Posts and Telecommunications (A2018-07)
  • Received Date: 2018-09-02
  • Rev Recd Date: 2019-02-26
  • Available Online: 2019-04-03
  • Publish Date: 2019-10-01
  • 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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  • TURKER B B, YEMEZ Y, SEZGIN T M, et al. Audio-facial laughter detection in naturalistic dyadic conversations[J]. IEEE Transactions on Affective Computing, 2017, 8(4): 534–545. doi: 10.1109/TAFFC.2017.2754256
    CHEN Min, HERRERA F, and HWANG K. Cognitive computing: Architecture, technologies and intelligent applications[J]. IEEE Access, 2018, 6: 19774–19783. doi: 10.1109/ACCESS.2018.2791469
    ZUCCO C, CALABRESE B, and CANNATARO M. Sentiment analysis and affective computing for depression monitoring[C]. The 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, USA, 2017: 1988–1995. doi: 10.1109/BIBM.2017.8217966.
    BELKAID M, CUPERLIER N, and GAUSSIER P. Autonomous cognitive robots need emotional modulations: Introducing the eMODUL model[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 49(1): 206–215. doi: 10.1109/TSMC.2018.2792542
    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
    LIU Xin, XIE Lun, and WANG Zhiliang. Empathizing with emotional robot based on cognition reappraisal[J]. China Communications, 2017, 14(9): 100–113. doi: 10.1109/CC.2017.8068769
    ZHANG Rui, WANG Zhenyu, and MAI Dongcheng. Building emotional conversation systems using multi-task Seq2Seq learning[C]. The Sixth CCF International Conference on Natural Language Processing and Chinese Computing, Dalian, China, 2017: 612–621. doi: 10.1007/978-3-319-73618-1_51.
    RODRÍGUEZ L F, GUTIERREZ-GARCIA J O, and RAMOS F. Modeling the interaction of emotion and cognition in Autonomous Agents[J]. Biologically Inspired Cognitive Architectures, 2016, 17: 57–70. doi: 10.1016/j.bica.2016.07.008
    NANTY A and GELIN R. Fuzzy controlled PAD emotional state of a NAO robot[C]. 2013 Conference on Technologies and Applications of Artificial Intelligence, Taipei, China, 2013: 90–96. doi: 10.1109/TAAI.2013.30.
    曹东岩. 基于强化学习的开放领域聊天机器人对话生成算法[D]. [硕士论文], 哈尔滨工业大学, 2017.

    CAO Dongyan. Research on reinforcement learning for open domain chatbot dialogue generation[D]. [Master dissertation], Harbin Institute of Technology, 2017.
    ZHOU Hao, HUANG Minlie, ZHANG Tianyang, et al. Emotional chatting machine: Emotional conversation generation with internal and external memory[C]. The Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, USA, 2018: 730–738.
    华生. 欲望心理学: 人际交往中的心理博弈[M]. 北京, 中央编译出版社, 2016: 1–5.

    HUA Sheng. Psychology on Desire: Psychological Game in Interpersonal Communication[M]. Beijing: Central Compilation & Translation Press, 2016: 1–5.
    卜湛, 伍之昂, 曹杰, 等. 在线评论情感计算与博弈预测[J]. 电子学报, 2015, 43(12): 2530–2535. doi: 10.3969/j.issn.0372-2112.2015.12.028

    BU Zhan, WU Zhiang, CAO Jie, et al. Affective computing and game theory based prediction for online reviews[J]. Acta Electronica Sinica, 2015, 43(12): 2530–2535. doi: 10.3969/j.issn.0372-2112.2015.12.028
    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.
    LI Jiaqi, ZHANG Chunyan, SUN Qinglin, et al. Changing the Intensity of Interaction Based on Individual Behavior in the Iterated Prisoner’s Dilemma Game[J]. IEEE Transactions on Evolutionary Computation, 2017, 21(4): 506–517. doi: 10.1109/TEVC.2016.2628385
    MARTINICH L P. Top ten lessons for managers: Deep dive into interpersonal communication[J]. IEEE Engineering Management Review, 2017, 45(2): 27–29. doi: 10.1109/EMR.2017.2701511
    SHANG Lifeng, LU Zhengdong, and LI Hang. Neural responding machine for short-text conversation[C]. The 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China, 2015: 1577-1586. doi: 10.3115/v1/p15-1152.
    COX G. ChatterBot tutorial[EB/OL]. https://chatterbot.readthedocs.io/en/stable/tutorial.html, 2018.
    SUTSKEVER I, VINYALS O, and LE Q V. Sequence to sequence learning with neural networks[C]. The 27th International Conference on Neural Information Processing Systems, Montreal, Canada, 2014: 3104–3112.
    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.
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