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Multi-platform Chatbot Modeling and Deployment with the Jarvis Framework

  • Gwendal DanielEmail author
  • Jordi Cabot
  • Laurent Deruelle
  • Mustapha Derras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)

Abstract

Chatbot applications are increasingly adopted in various domains such as e-commerce or customer services as a direct communication channel between companies and end-users. Multiple frameworks have been developed to ease their definition and deployment. They typically rely on existing cloud infrastructures and artificial intelligence techniques to efficiently process user inputs and extract conversation information. While these frameworks are efficient to design simple chatbot applications, they still require advanced technical knowledge to define complex conversations and interactions. In addition, the deployment of a chatbot application usually requires a deep understanding of the targeted platforms, increasing the development and maintenance costs. In this paper we introduce the Jarvis framework, that tackles these issues by providing a Domain Specific Language (DSL) to define chatbots in a platform-independent way, and a runtime engine that automatically deploys the chatbot application and manages the defined conversation logic. Jarvis is open source and fully available online.

Keywords

MDE DSL Chatbot design Chatbot deployment 

Notes

Acknowledgement

This work has been partially funded by the Electronic Component Systems for European Leadership Joint Undertaking under grant management No. 737494 (MegaMRt2 project) and the Spanish government (TIN2016-75944-R project).

References

  1. 1.
    Amazon: Amazon Lex Website (2018). https://aws.amazon.com/lex/
  2. 2.
    Bettini, L.: Implementing Domain-Specific Languages with Xtext and Xtend. Packt Publishing Ltd., Birmingham (2013)Google Scholar
  3. 3.
    Botkit: Botkit Website (2018). https://botkit.ai
  4. 4.
    Brambilla, M., Cabot, J., Wimmer, M.: Model-driven software engineering in practice. Synth. Lect. Softw. Eng. 1(1), 1–182 (2012)CrossRefGoogle Scholar
  5. 5.
    Chatfuel: Chatfuel Website (2018). https://chatfuel.com/
  6. 6.
    Clements, P., Northrop, L.: Software Product Lines: Practices and Patterns, vol. 3. Addison-Wesley, Reading (2002)Google Scholar
  7. 7.
    FlowXO: FlowXO Website (2019). https://flowxo.com/
  8. 8.
    Google: DialogFlow Website (2018). https://dialogflow.com/
  9. 9.
    Grinter, R.E., Palen, L.: Instant messaging in teen life. In: Proceedings of the 5th CSCW Conference, pp. 21–30. ACM (2002)Google Scholar
  10. 10.
    Hutchinson, J., Whittle, J., Rouncefield, M.: Model-driven engineering practices in industry: social, organizational and managerial factors that lead to success or failure. SCP 89, 144–161 (2014)Google Scholar
  11. 11.
    IBM: Watson Assistant Website (2018). https://www.ibm.com/watson/ai-assistant/
  12. 12.
    Gartner Inc.: The Road to Enterprise AI. RAGE Frameworks (2017)Google Scholar
  13. 13.
    Jackson, P., Moulinier, I.: Natural Language Processing for Online Applications: Text Retrieval, Extraction and Categorization, vol. 5. John Benjamins Publishing, Amsterdam (2007)CrossRefGoogle Scholar
  14. 14.
    Jennings, N.R., Wooldridge, M.: Agent-oriented software engineering. In: Handbook of Agent Technology, vol. 18 (2001)Google Scholar
  15. 15.
    Kavaler, D., Sirovica, S., Hellendoorn, V., Aranovich, R., Filkov, V.: Perceived language complexity in GitHub issue discussions and their effect on issue resolution. In: Proceedings of the 32nd ASE Conference, pp. 72–83. IEEE (2017)Google Scholar
  16. 16.
    Kerlyl, A., Hall, P., Bull, S.: Bringing Chatbots into Education: Towards Natural Language Negotiation of Open Learner Models. In: Ellis, R., Allen, T., Tuson, A. (eds.) Applications and Innovations in Intelligent Systems XIV, pp. 179–192. Springer, London (2007).  https://doi.org/10.1007/978-1-84628-666-7_14CrossRefGoogle Scholar
  17. 17.
    Kleppe, A.: Software Language Engineering: Creating Domain-Specific Languages Using Metamodels. Pearson Education, London (2008)Google Scholar
  18. 18.
    Klopfenstein, L.C., Delpriori, S., Malatini, S., Bogliolo, A.: The rise of bots: a survey of conversational interfaces, patterns, and paradigms. In: Proceedings of the 12th DIS Conference, pp. 555–565. ACM (2017)Google Scholar
  19. 19.
    Masche, J., Le, N.-T.: A review of technologies for conversational systems. In: Le, N.-T., Van Do, T., Nguyen, N.T., Thi, H.A.L. (eds.) ICCSAMA 2017. AISC, vol. 629, pp. 212–225. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-61911-8_19CrossRefGoogle Scholar
  20. 20.
    Mayo, J.: Programming the Microsoft Bot Framework: A Multiplatform Approach to Building Chatbots. Microsoft Press, Redmond (2017)Google Scholar
  21. 21.
    Mendix: Mendix Website (2018). https://www.mendix.com/
  22. 22.
    Nardi, B.A., Whittaker, S., Bradner, E.: Interaction and outeraction: instant messaging in action. In: Proceedings of the 3rd CSCW Conference, pp. 79–88. ACM (2000)Google Scholar
  23. 23.
    Pereira, J., Díaz, Ó.: A quality analysis of Facebook Messenger’s most popular chatbots. In: Proceedings of the 33rd SAC Symposium, pp. 2144–2150. ACM (2018)Google Scholar
  24. 24.
    Pereira, J., Díaz, Ó.: Chatbot dimensions that matter: lessons from the trenches. In: Mikkonen, T., Klamma, R., Hernández, J. (eds.) ICWE 2018. LNCS, vol. 10845, pp. 129–135. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-91662-0_9CrossRefGoogle Scholar
  25. 25.
    Radziwill, N.M., Benton, M.C.: Evaluating quality of chatbots and intelligent conversational agents. arXiv preprint arXiv:1704.04579 (2017)
  26. 26.
    Rozsnyai, S., Schiefer, J., Schatten, A.: Concepts and models for typing events for event-based systems. In: Proceedings of the 1st DEBS Conference, pp. 62–70. ACM (2007)Google Scholar
  27. 27.
    Shawar, A., Atwell, E., Roberts, A.: FAQchat as in information retrieval system. In: Proceedings of the 2nd LTC Conference, pp. 274–278. Wydawnictwo Poznanskie, Poznan (2005)Google Scholar
  28. 28.
    Smartloop: Smartloop Website (2018). https://smartloop.ai/
  29. 29.
    Subrahmanian, V.S., et al.: The DARPA Twitter bot challenge. arXiv preprint arXiv:1601.05140 (2016)
  30. 30.
    Thomas, N.T.: An E-business chatbot using AIML and LSA. In: Proceedings of the 5th ICACCI Conference, pp. 2740–2742. IEEE (2016)Google Scholar
  31. 31.
    Wieringa, R.J.: Design Science Methodology for Information Systems and Software Engineering. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-43839-8CrossRefGoogle Scholar
  32. 32.
    Xu, A., Liu, Z., Guo, Y., Sinha, V., Akkiraju, R.: A new chatbot for customer service on social media. In: Proceedings of the 35th CHI Conference, pp. 3506–3510. ACM (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gwendal Daniel
    • 1
    Email author
  • Jordi Cabot
    • 1
    • 2
  • Laurent Deruelle
    • 3
  • Mustapha Derras
    • 4
  1. 1.Internet Interdisciplinary Institute (IN3)Universitat Oberta de Catalunya (UOC)BarcelonaSpain
  2. 2.ICREABarcelonaSpain
  3. 3.Berger-LevraultPérolsFrance
  4. 4.Berger-LevraultLabègeFrance

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