Advertisement

A Multimodal Chatbot System for Enhancing Social Skills Training for Security Guards

  • Stein de Bever
  • Daniel FormoloEmail author
  • Shuai Wang
  • Tibor Bosse
Conference paper
  • 1.1k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11566)

Abstract

Chatbots are typically used in dialogue systems for various purposes such as customer service and information acquisition. This paper explores enhancement of social skills training for security guards with the use of chatbots. More specifically, we designed a chatbot using text and voice as input to study the acceptance and the impact of the system to training security guards in deal with stress situations. The result of a pilot experiment and a survey are presented and discussed. Finally, we discuss possible improvements and future work.

Keywords

Chatbots Serious games Training Role-playing Security employees Intelligent agents 

Notes

Acknowledgement

This research was supported by the Brazilian scholarship program Science without Borders - CNPq scholarship reference: 233883/2014-2.

References

  1. 1.
    Abbasi, S., Kazi, H.: Measuring effectiveness of learning chatbot systems on student’s learning outcome and memory retention. Asian J. Appl. Sci. Eng. 3(2), 251–260 (2014)Google Scholar
  2. 2.
    Bickmore, T.W., Puskar, K., Schlenk, E.A., Pfeifer, L.M., Sereika, S.M.: Maintaining reality: relational agents for antipsychotic medication adherence. Interact. Comput. 22(4), 276–288 (2010)CrossRefGoogle Scholar
  3. 3.
    Bosse, T., Gerritsen, C., de Man, J.: Evaluation of a virtual training environment for aggression de-escalation. In: Proceedings of Game-On, pp. 48–58 (2015)Google Scholar
  4. 4.
    Bosse, T., Provoost, S.: Towards aggression de-escalation training with virtual agents: a computational model. In: Zaphiris, P., Ioannou, A. (eds.) LCT 2014. LNCS, vol. 8524, pp. 375–387. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07485-6_37CrossRefGoogle Scholar
  5. 5.
    Hoffmann, R., Kowalski, S., Jain, R., Mumtaz, M.: E\(\_\)universities services in the new social eco-systems: Security risk analysis: Using conversational agents to help teach information security risk analysis (2011)Google Scholar
  6. 6.
    Medeiros, L., Bosse, T.: Testing the acceptability of social support agents in online communities. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10448, pp. 125–136. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67074-4_13CrossRefGoogle Scholar
  7. 7.
    Oh, K.J., Lee, D., Ko, B., Choi, H.J.: A chatbot for psychiatric counseling in mental healthcare service based on emotional dialogue analysis and sentence generation. In: 2017 18th IEEE International Conference on Mobile Data Management (MDM), pp. 371–375. IEEE (2017)Google Scholar
  8. 8.
    Schreiber, J.B., Nora, A., Stage, F.K., Barlow, E.A., King, J.: Reporting structural equation modeling and confirmatory factor analysis results: a review. J. Educ. Res. 99(6), 323–338 (2006)CrossRefGoogle Scholar
  9. 9.
    Shawar, B.A.A., Atwell, E.: A corpus based approach to generalising a chatbot system. Ph.D. thesis, University of Leeds (School of Computing) (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Stein de Bever
    • 1
  • Daniel Formolo
    • 1
    Email author
  • Shuai Wang
    • 1
  • Tibor Bosse
    • 2
  1. 1.Department of Computer ScienceVrije UniversiteitAmsterdamThe Netherlands
  2. 2.Behavioural Science InstituteRadboud UniversiteitNijmegenThe Netherlands

Personalised recommendations