Skip to main content

A Novel Multi-agent-based Chatbot Approach to Orchestrate Conversational Assistants

  • Conference paper
  • First Online:
Business Information Systems (BIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 389))

Included in the following conference series:

  • 1653 Accesses

Abstract

Nowadays, chatbots have become more and more prominent in various domains. Nevertheless, designing a versatile chatbot, giving reasonable answers, is a challenging task. Thereby, the major drawback of most chatbots is their limited scope. Multi-agent-based systems offer approaches to solve problems in a cooperative manner following the “divide and conquer” paradigm. Consequently, it seems promising to design a multi-agent-based chatbot approach scaling beyond the scope of a single application context. To address this research gap, we propose a novel approach orchestrating well-established conversational assistants. We demonstrate and evaluate our approach using six chatbots, providing higher quality than competing artifacts.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahmad, N.A., Che, M.H., Zainal, A., et al.: Review of chatbots design techniques. IJACSA 181(8), 7–10 (2018)

    Google Scholar 

  2. Klopfenstein, L.C., Delpriori, S., Malatini, S., et al.: The rise of bots: a survey of conversational interfaces, patterns, and paradigms. In: Proceedings of the 12th Conference on Designing Interactive Systems, pp. 555–565 (2017)

    Google Scholar 

  3. Chaves, A.P., Gerosa, M.A.: Single or multiple conversational agents? An interactional coherence comparison. In: Proceedings of the 36th CHI (2018)

    Google Scholar 

  4. Masche, J., Le, N.-T.: A review of technologies for conversational systems. In: Proceedings of the 5th ICCSAMA, pp. 212–225 (2017)

    Google Scholar 

  5. Dhanda, S.: How chatbots will transform the retail industry. Juniper Research (2018)

    Google Scholar 

  6. Abdul-Kader, S.A., Woods, J.C.: Survey on chatbot design techniques in speech conversation systems. IJACSA 6(7), 72–80 (2015)

    Google Scholar 

  7. Chen, H., Liu, X., Yin, D., et al.: A survey on dialogue systems: recent advances and new frontiers. ACM SIGKDD Explor. Newslett. 19(2), 25–35 (2017)

    Article  Google Scholar 

  8. Ramesh, K., Ravishankaran, S., Joshi, A., Chandrasekaran, K.: A survey of design techniques for conversational agents. In: Kaushik, S., Gupta, D., Kharb, L., Chahal, D. (eds.) ICICCT 2017. CCIS, vol. 750, pp. 336–350. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6544-6_31

    Chapter  Google Scholar 

  9. Wallace, R.S.: The anatomy of ALICE. In: Epstein, R., Roberts, G., Beber, G. (eds.) Parsing the Turing Test, pp. 181–210. Springer, Dordrecht (2009). https://doi.org/10.1007/978-1-4020-6710-5_13

    Chapter  Google Scholar 

  10. Serban, I.V., Sankar, C., Germain, M., et al.: A deep reinforcement learning chatbot (2017)

    Google Scholar 

  11. Pichl, J., Marek, P., Konrád, J., et al.: Alquist: the Alexa prize socialbot. In: Proceedings of the 1st Alexa Prize (2017)

    Google Scholar 

  12. Huang, T.-H.K., Chang, J.C., Bigham, J.P.: Evorus: a crowd-powered conversational assistant built to automate itself over time. In: Proceedings of the 36th CHI (2018)

    Google Scholar 

  13. Papaioannou, I., Curry, A.C., Part, J.L., et al.: Alana: social dialogue using an ensemble model and a ranker trained on user feedback. In: Proceedings of the 1st Alexa Prize (2017)

    Google Scholar 

  14. Pinhanez, C.S., Candello, H., Pichiliani, M.C., et al.: Different but equal: comparing user collaboration with digital personal assistants vs. teams of expert agents (2018)

    Google Scholar 

  15. Janarthanam, S.: Hands-On Chatbots and Conversational UI Development. Packt Publishing, Birmingham (2017)

    Google Scholar 

  16. Chandar, P., et al.: Leveraging conversational systems to assists new hires during onboarding. In: Bernhaupt, R., Dalvi, G., Joshi, A., Balkrishan, D., O’Neill, J., Winckler, M. (eds.) INTERACT 2017. LNCS, vol. 10514, pp. 381–391. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67684-5_23

    Chapter  Google Scholar 

  17. Jennings, N.R.: Commitments and conventions: the foundation of coordination in multi-agent systems. Knowl. Eng. Rev. 8(3), 223–250 (1993)

    Article  MathSciNet  Google Scholar 

  18. Jennings, N.R.: An agent-based approach for building complex software systems. Commun. ACM 44(4), 35–41 (2001)

    Article  Google Scholar 

  19. Klusch, M., Sycara, K.: Brokering and matchmaking for coordination of agent societies. a survey. In: Omicini, A., Zambonelli, F., Klusch, M. (eds.) Coordination of Internet Agents, pp. 197–224. Springer, Heidelberg (2001). https://doi.org/10.1007/978-3-662-04401-8_8

  20. Peffers, K., Tuunanen, T., Rothenberger, M.A., et al.: A design science research methodology for information systems research. JMIS 24(3), 45–77 (2007)

    Google Scholar 

  21. Maglio, P.P., Matlock, T., Campbell, C.S., Zhai, S., Smith, B.A.: Gaze and speech in attentive user interfaces. In: Tan, T., Shi, Y., Gao, W. (eds.) ICMI 2000. LNCS, vol. 1948, pp. 1–7. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-40063-X_1

    Chapter  Google Scholar 

  22. Cui, L., Huang, S., Wei, F., et al.: Superagent. A customer service chatbot for e-commerce websites. In: Proceedings of the 55th Annual Meeting of the ACL, pp. 97–102 (2017)

    Google Scholar 

  23. Arentze, T., Timmermans, H.: Modeling the formation of activity agendas using reactive agents. Environ. Plan. B 29(5), 719–728 (2002)

    Article  Google Scholar 

  24. Ehlert, P., Rothkrantz, L.J.M.: Microscopic traffic simulation with reactive driving agents. In: 4th Proceedings of IEEE Intelligent Transportation Systems, pp. 861–866 (2001)

    Google Scholar 

  25. Rao, A.S., Georgeff, M.P.: BDI agents. In: 1st ICMAS, pp. 312–319 (1995)

    Google Scholar 

  26. Barua, A., Whinston, A.B., Yin, F.: Value and productivity in the internet economy. Computer 33(5), 102–105 (2000)

    Article  Google Scholar 

  27. Decker, K., Sycara, K., Williamson, M.: Middle-agents for the internet. In: Proceedings of the 15th IJCAI, pp. 578–583 (1997)

    Google Scholar 

  28. Hettige, B., Karunananda, A.S.: Octopus: a multi agent chatbot. In: Proceedings of the 8th International Research Conference, pp. 41–47 (2015)

    Google Scholar 

  29. Gregor, S., Hevner, A.R.: Positioning and presenting design science research for maximum impact. MIS Q. 37, 337–355 (2013)

    Article  Google Scholar 

  30. Baskerville, R., Baiyere, A., Gregor, S., et al.: Design science research contributions: finding a balance between artifact and theory. JAIS 19, 358–376 (2018)

    Article  Google Scholar 

  31. Hevner, A.R., March, S.T., Park, J., et al.: Design science in information systems research. MIS Q. 28, 75–105 (2004)

    Article  Google Scholar 

  32. Labrou, Y., Finin, T., Peng, Y.: Agent communication languages: the current landscape. Intell. Syst. Appl. 14(2), 45–52 (1999)

    Article  Google Scholar 

  33. Park, S., An, D.U.: Automatic e-mail classification using dynamic category hierarchy and semantic features. IETE Tech. Rev. 27(6), 478–492 (2010)

    Article  Google Scholar 

  34. Li, N., Wu, D.D.: Using text mining and sentiment analysis for online forums hotspot detection and forecast. DSS 48(2), 354–368 (2010)

    Google Scholar 

  35. Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997). https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  36. Russell, S.J., Norvig, P.: AI. A Modern Approach. Pearson Education, London (2010)

    Google Scholar 

  37. Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Sattar, A., Kang, B. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1015–1021. Springer, Heidelberg (2006). https://doi.org/10.1007/11941439_114

    Chapter  Google Scholar 

  38. Skorochod’ko, E.F.: Adaptive method of automatic abstracting and indexing. In: Proceedings of the 5th Information Processing Congress, pp. 1179–1182 (1972)

    Google Scholar 

  39. Beeferman, D., Berger, A., Lafferty, J.: Statistical models for text segmentation. Mach. Learn. 34(1–3), 177–210 (1999). https://doi.org/10.1023/A:1007506220214

    Article  MATH  Google Scholar 

  40. Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)

    Article  Google Scholar 

  41. Fikes, R.E., Nilsson, N.J.: STRIPS: a new approach to the application of theorem proving to problem solving. Artif. Intell. 2(3–4), 189–208 (1971)

    Article  Google Scholar 

  42. Dang, V., Croft, B.W.: Query reformulation using anchor text. In: Proceedings of the 3rd WSDM, pp. 41–50 (2010)

    Google Scholar 

  43. Mitsuku Dataset. https://github.com/pandorabots/Free-AIML. Accessed 06 Dec 2019

  44. Rosie Dataset. https://github.com/pandorabots/rosie. Accessed 06 Dec 2019

  45. Quora Dataset. https://www.kaggle.com/c/quora-question-pairs. Accessed 06 Dec 2019

  46. Wikipedia Dataset. https://www.kaggle.com/rtatman/questionanswer-dataset. Accessed 06 Dec 2019

  47. Ling, W., Yogatama, D., Dyer, C., et al.: Program induction by rationale generation: learning to solve and explain algebraic word problems. In: Proceedings of the 55th Annual Meeting of the ACL, pp. 158–167 (2017)

    Google Scholar 

  48. Bedué, P., Graef, R., Klier, M., et al.: A novel hybrid knowledge retrieval approach for online customer service platforms. In: Proceedings of the 26th ECIS (2018)

    Google Scholar 

  49. Aimpulse Spectrum. https://developer.aimpulse.com. Accessed 23 Aug 2019

  50. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th IJCAI, vol. 14, no. 2, pp. 1137–1145 (1995)

    Google Scholar 

  51. Venable, J., Pries-Heje, J., Baskerville, R.: FEDS: a framework for evaluation in design science research. Eur. J. Inf. Syst. 25, 77–89 (2016)

    Article  Google Scholar 

  52. Stoeckli, E., Uebernickel, F., Brenner, W.: Exploring affordances of slack integrations and their actualization within enterprises-towards an understanding of how chatbots create value. In: Proceedings of the 51st HICSS (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jan Felix Zolitschka .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zolitschka, J.F. (2020). A Novel Multi-agent-based Chatbot Approach to Orchestrate Conversational Assistants. In: Abramowicz, W., Klein, G. (eds) Business Information Systems. BIS 2020. Lecture Notes in Business Information Processing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-53337-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-53337-3_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-53336-6

  • Online ISBN: 978-3-030-53337-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics