Research on Active Interaction Design for Smart Speakers Agent of Home Service Robot

  • Jingyan QinEmail author
  • Zhibo Chen
  • Wenhao Zhang
  • Daisong Guan
  • Zhun Wu
  • Min Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11584)


With the smart speakers agent of Home Service Robot represented by voice interaction, tangible user interface interaction and somatosensory interaction are widely present in family environment and serve multiple family members, the trustworthy AI stimulates the transition of the interaction form from passive interaction to proactive interaction, finally into active interaction. However, with the personalization of family members’ needs, the improvement of emotional needs lead to user low patience and high expectations toward the home service robots, the traditional passive interaction has met the above changes of users. This paper proposes the active interaction design method to enhance the initiate of the intelligent agents to solve the user’s needs, improve Interaction performance and user experience. This paper uses questionnaire analysis, user interview, expert cognitive walkthrough, field survey, and comparative research to conduct research. Through the comparative study of passive interaction, proactive interaction and active interaction, the computational analysis, context awareness, consciousness awareness and emotion analysis, combined with the actual case of Baidu smart speakers project, the author put forward active interaction model and the active interaction design form of the family agent. Apply it to the family situation and gradually improve the active interaction research of the home service robot in the family environment.


Active interaction Home service robot Interaction design 


  1. 1.
    Begole, B.: Responsive media: media experiences in the age of thinking machines. APSIPA Trans. on Signal Inf. Process., 6 (2017)Google Scholar
  2. 2.
    Dasgupta, I., Wang, J., Chiappa S., et al.: Causal Reasoning from Meta-reinforcement Learning. arXiv preprint arXiv:1901.08162 (2019)
  3. 3.
    Hermans B.: Intelligent Software Agents on the Internet: Chapters 6–7. First Monday, vol. 2(3) (1997)Google Scholar
  4. 4.
    Wooldridge, M., Jennings, N.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995). Scholar
  5. 5.
    Da, Z., Lu, C.: Interaction between human and agent: artificial intelligence system design related to people. Decoration 11, 14–21 (2016)Google Scholar
  6. 6.
    Zhao, L., Hou, Y.: Intelligent software: from object-oriented to agent-oriented. Comput. Eng. Appl., (05), 41–43+125 (2001)Google Scholar
  7. 7.
    Chen, G., Lu, R.Q.: The relation Web model: an organizational approach to agent cooperation based on social mechanism. J. Comput. Res. Dev. 40(1), 107–114 (2003)MathSciNetGoogle Scholar
  8. 8.
    Hämäläinen, V.P.: Usability testing methodology of proactive HMIs for virtual control room, pp. 13–14 (2014)Google Scholar
  9. 9.
    De Boeck, J., Verpoorten, K., Luyten, K., Coninx, K.: A Comparison between Decision Trees and Markov Models to Support Proactive Interfaces. In: 18th International Conference on Database and Expert Systems Applications (DEXA 2007), pp. 94–98 (2007)Google Scholar
  10. 10.
    Tennenhouse, D.: Proactive computing. Commun. ACM 43(5), 43–50 (2000)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jingyan Qin
    • 1
    Email author
  • Zhibo Chen
    • 1
  • Wenhao Zhang
    • 1
  • Daisong Guan
    • 2
  • Zhun Wu
    • 2
  • Min Zhao
    • 2
  1. 1.School of Mechanical EngineeringUniversity of Science and TechnologyBeijingPeople’s Republic of China
  2. 2.Baidu AI Interaction Design LabBeijingPeople’s Republic of China

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