Log in Menu Search Cart Search Home Business Information Systems Conference paper pp 103–117 Business Information Systems (BIS 2020) ORCID: orcid.org/0000-0001-5754-3207 ORCID: Part of the book series: Accesses Log in via an institution Springer+ Basic Subscribe now Chapter © 2021 Article Open access 01 June 2023 Chapter © 2023 Authors Jan Felix Zolitschka PubMed Google Scholar : https://doi.org/10.1007/978-3-030-53337-3_8 : 22 July 2020 : Springer, Cham : 978-3-030-53336-6 : 978-3-030-53337-3 : Computer Science Computer Science (R0) Log in via an institution Springer+ Basic Subscribe now Your privacy choices/Manage cookies 47.236.104.153 We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media. By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection. See our privacy policy for more information on the use of your personal data. Manage preferences for further information and to change your choices. Advertisement Part of the book series:Lecture Notes in Business Information Processing ((LNBIP,volume 389)) Included in the following conference series: 1720 Accesses 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. Tax calculation will be finalised at checkout Purchases are for personal use only Institutional subscriptions Ahmad, N.A., Che, M.H., Zainal, A., et al.: Review of chatbots design techniques. IJACSA 181(8), 7–10 (2018) Google Scholar 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 Chaves, A.P., Gerosa, M.A.: Single or multiple conversational agents? An interactional coherence comparison. In: Proceedings of the 36th CHI (2018) Google Scholar Masche, J., Le, N.-T.: A review of technologies for conversational systems. In: Proceedings of the 5th ICCSAMA, pp. 212–225 (2017) Google Scholar Dhanda, S.: How chatbots will transform the retail industry. Juniper Research (2018) Google Scholar Abdul-Kader, S.A., Woods, J.C.: Survey on chatbot design techniques in speech conversation systems. IJACSA 6(7), 72–80 (2015) Google Scholar 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 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 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 Serban, I.V., Sankar, C., Germain, M., et al.: A deep reinforcement learning chatbot (2017) Google Scholar Pichl, J., Marek, P., Konrád, J., et al.: Alquist: the Alexa prize socialbot. In: Proceedings of the 1st Alexa Prize (2017) Google Scholar 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 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 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 Janarthanam, S.: Hands-On Chatbots and Conversational UI Development. Packt Publishing, Birmingham (2017) Google Scholar 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 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 Jennings, N.R.: An agent-based approach for building complex software systems. Commun. ACM 44(4), 35–41 (2001) Article  Google Scholar 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 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 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 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 Arentze, T., Timmermans, H.: Modeling the formation of activity agendas using reactive agents. Environ. Plan. B 29(5), 719–728 (2002) Article  Google Scholar 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 Rao, A.S., Georgeff, M.P.: BDI agents. In: 1st ICMAS, pp. 312–319 (1995) Google Scholar Barua, A., Whinston, A.B., Yin, F.: Value and productivity in the internet economy. Computer 33(5), 102–105 (2000) Article  Google Scholar Decker, K., Sycara, K., Williamson, M.: Middle-agents for the internet. In: Proceedings of the 15th IJCAI, pp. 578–583 (1997) Google Scholar Hettige, B., Karunananda, A.S.: Octopus: a multi agent chatbot. In: Proceedings of the 8th International Research Conference, pp. 41–47 (2015) Google Scholar Gregor, S., Hevner, A.R.: Positioning and presenting design science research for maximum impact. MIS Q. 37, 337–355 (2013) Article  Google Scholar 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 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 Labrou, Y., Finin, T., Peng, Y.: Agent communication languages: the current landscape. Intell. Syst. Appl. 14(2), 45–52 (1999) Article  Google Scholar 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 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 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 Russell, S.J., Norvig, P.: AI. A Modern Approach. Pearson Education, London (2010) Google Scholar 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 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 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 Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995) Article  Google Scholar 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 Dang, V., Croft, B.W.: Query reformulation using anchor text. In: Proceedings of the 3rd WSDM, pp. 41–50 (2010) Google Scholar Mitsuku Dataset. https://github.com/pandorabots/Free-AIML. Accessed 06 Dec 2019 Rosie Dataset. https://github.com/pandorabots/rosie. Accessed 06 Dec 2019 Quora Dataset. https://www.kaggle.com/c/quora-question-pairs. Accessed 06 Dec 2019 Wikipedia Dataset. https://www.kaggle.com/rtatman/questionanswer-dataset. Accessed 06 Dec 2019 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 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 Aimpulse Spectrum. https://developer.aimpulse.com. Accessed 23 Aug 2019 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 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 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 University Ulm, Helmholtzstraße 22, 89081, Ulm, Germany Jan Felix Zolitschka You can also search for this author in PubMed Google Scholar Correspondence to Jan Felix Zolitschka . Poznań University of Economics and Business, Poznan, Poland Witold Abramowicz University of Colorado, Colorado Springs, CO, USA Gary Klein Reprints and permissions © 2020 Springer Nature Switzerland AG 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 DOI: https://doi.org/10.1007/978-3-030-53337-3_8 Published: 22 July 2020 Publisher Name: Springer, Cham Print ISBN: 978-3-030-53336-6 Online ISBN: 978-3-030-53337-3 eBook Packages: Computer ScienceComputer Science (R0) Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Policies and ethics Tax calculation will be finalised at checkout Purchases are for personal use only Institutional subscriptions Ahmad, N.A., Che, M.H., Zainal, A., et al.: Review of chatbots design techniques. IJACSA 181(8), 7–10 (2018) Google Scholar 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 Chaves, A.P., Gerosa, M.A.: Single or multiple conversational agents? An interactional coherence comparison. In: Proceedings of the 36th CHI (2018) Google Scholar Masche, J., Le, N.-T.: A review of technologies for conversational systems. In: Proceedings of the 5th ICCSAMA, pp. 212–225 (2017) Google Scholar Dhanda, S.: How chatbots will transform the retail industry. Juniper Research (2018) Google Scholar Abdul-Kader, S.A., Woods, J.C.: Survey on chatbot design techniques in speech conversation systems. IJACSA 6(7), 72–80 (2015) Google Scholar 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 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 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 Serban, I.V., Sankar, C., Germain, M., et al.: A deep reinforcement learning chatbot (2017) Google Scholar Pichl, J., Marek, P., Konrád, J., et al.: Alquist: the Alexa prize socialbot. In: Proceedings of the 1st Alexa Prize (2017) Google Scholar 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 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 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 Janarthanam, S.: Hands-On Chatbots and Conversational UI Development. Packt Publishing, Birmingham (2017) Google Scholar 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 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 Jennings, N.R.: An agent-based approach for building complex software systems. Commun. ACM 44(4), 35–41 (2001) Article  Google Scholar 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 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 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 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 Arentze, T., Timmermans, H.: Modeling the formation of activity agendas using reactive agents. Environ. Plan. B 29(5), 719–728 (2002) Article  Google Scholar 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 Rao, A.S., Georgeff, M.P.: BDI agents. In: 1st ICMAS, pp. 312–319 (1995) Google Scholar Barua, A., Whinston, A.B., Yin, F.: Value and productivity in the internet economy. Computer 33(5), 102–105 (2000) Article  Google Scholar Decker, K., Sycara, K., Williamson, M.: Middle-agents for the internet. In: Proceedings of the 15th IJCAI, pp. 578–583 (1997) Google Scholar Hettige, B., Karunananda, A.S.: Octopus: a multi agent chatbot. In: Proceedings of the 8th International Research Conference, pp. 41–47 (2015) Google Scholar Gregor, S., Hevner, A.R.: Positioning and presenting design science research for maximum impact. MIS Q. 37, 337–355 (2013) Article  Google Scholar 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 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 Labrou, Y., Finin, T., Peng, Y.: Agent communication languages: the current landscape. Intell. Syst. Appl. 14(2), 45–52 (1999) Article  Google Scholar 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 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 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 Russell, S.J., Norvig, P.: AI. A Modern Approach. Pearson Education, London (2010) Google Scholar 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 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 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 Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995) Article  Google Scholar 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 Dang, V., Croft, B.W.: Query reformulation using anchor text. In: Proceedings of the 3rd WSDM, pp. 41–50 (2010) Google Scholar Mitsuku Dataset. https://github.com/pandorabots/Free-AIML. Accessed 06 Dec 2019 Rosie Dataset. https://github.com/pandorabots/rosie. Accessed 06 Dec 2019 Quora Dataset. https://www.kaggle.com/c/quora-question-pairs. Accessed 06 Dec 2019 Wikipedia Dataset. https://www.kaggle.com/rtatman/questionanswer-dataset. Accessed 06 Dec 2019 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 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 Aimpulse Spectrum. https://developer.aimpulse.com. Accessed 23 Aug 2019 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 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 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 47.236.104.153 Not affiliated © 2025 Springer Nature