A Survey on Conversational Agents/Chatbots Classification and Design Techniques

  • Shafquat HussainEmail author
  • Omid Ameri Sianaki
  • Nedal Ababneh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)


A chatbot can be defined as a computer program, designed to interact with users using natural language or text in a way that the user thinks he is having dialogue with a human. Most of the chatbots utilise the algorithms of artificial intelligence (AI) in order to generate required response. Earlier chatbots merely created an illusion of intelligence by employing much simpler pattern matching and string processing design techniques for their interaction with users using rule-based and generative-based models. However, with the emergence of new technologies more intelligent systems have emerged using complex knowledge-based models. This paper aims to discuss chatbots classification, their design techniques used in earlier and modern chatbots and how the two main categories of chatbots handle conversation context.


Chatbots Conversational agents Classifications Design techniques conversational context Algorithms Rule based Retrieval based Generative based Chatscript AIML Pattern matching Machine learning Neural network 


  1. 1.
    Zadrozny, W., et al.: Natural language dialogue for personalized interaction. Commun. ACM 43(8), 116–120 (2000)CrossRefGoogle Scholar
  2. 2.
    Hussain, S., Athula, G.: Extending a conventional chatbot knowledge base to external knowledge source and introducing user based sessions for diabetes education. In: 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA) 2018. IEEEGoogle Scholar
  3. 3.
    Levesque, H.J.: Common sense, the turing test, and the quest for real AI: Reflections on Natural and Artificial Intelligence. MIT Press, Cambridge (2017)Google Scholar
  4. 4.
    Wikipedia: Chatbot. 29 December 2018 (cited 30 December 2018).
  5. 5.
    Weizenbaum, J.: ELIZA–a computer program for the study of natural language communication between man and machine. Commun. ACM 26(1), 23–28 (1983)CrossRefGoogle Scholar
  6. 6.
    Wikipedia. Artificial Linguistic Internet Computer Entity. 19 November 2018 (cited 30 December 2018).
  7. 7.
    Wallace, R.S.: The anatomy of ALICE. In: Parsing the Turing Test, pp. 181–210. Springer, Dordrecht (2009)Google Scholar
  8. 8.
    Ramesh, K., et al.: A Survey of Design Techniques for Conversational Agents. In: International Conference on Information, Communication and Computing Technology. Springer, Singapore (2017)Google Scholar
  9. 9.
    Budulan, S.: Chatbot Categories and Their Limitations (2018). (cited May 2018).
  10. 10.
    Chen, H., et al.: A Survey on Dialogue Systems: Recent Advances and New Frontiers (2017). arXiv preprint arXiv:1711.01731
  11. 11.
    Yan, Z., et al.: Building Task-Oriented Dialogue Systems for Online Shopping. In: AAAI (2017)Google Scholar
  12. 12.
    Masche, J., Le, N.-T.: A Review of Technologies for Conversational Systems. In: International Conference on Computer Science, Applied Mathematics and Applications. Springer, Cham (2017)Google Scholar
  13. 13.
    Abdul-Kader, S.A., Woods, J.: Survey on chatbot design techniques in speech conversation systems. Int. J. Adv. Comput. Sci. Appl. 6(7), 72–80 (2015)Google Scholar
  14. 14.
    Mathur, V., Singh, A.: The Rapidly Changing Landscape of Conversational Agents (2018). arXiv preprint arXiv:1803.08419
  15. 15.
    Bradeško, L., Mladenić, D.: A survey of chatbot systems through a loebner prize competition. In: Proceedings of Slovenian Language Technologies Society Eighth Conference of Language Technologies (2012)Google Scholar
  16. 16.
    Weizenbaum, J.: ELIZA—a computer program for the study of natural language communication between man and machine. Commun. ACM 9(1), 36–45 (1966)CrossRefGoogle Scholar
  17. 17.
    Clarke, D.: Three AI Technologies that could make chatbots intelligent. 24 March 2018 (cited 18 December 2018).
  18. 18.
    Shawar, B.A., Atwell, E.: Chatbots: are they really useful? Zeitschrift für Computerlinguistik und Sprachtechnologie, p. 29 (2007)Google Scholar
  19. 19.
    Wikipedia contributors. ChatScript. Wikipedia, The Free Encyclopedia. 4 September 2018, 19:19 UTC. Accessed 22 Jan 2019
  20. 20.
    Robino, G.: How to build your first chatbot using Chatscript (2018). (cited 18 December 2018).
  21. 21.
    Milward, D., Beveridge, M.: Ontology-based dialogue systems. In: Proceedings of 3rd Workshop on Knowledge and reasoning in practical dialogue systems (IJCAI 2003) 2003Google Scholar
  22. 22.
    Al-Zubaide, H., Issa, A.A.: Ontbot: Ontology based chatbot. In: 2011 Fourth International Symposium on Innovation in Information & Communication Technology (ISIICT). IEEE (2011)Google Scholar
  23. 23.
    Nuez Ezquerra, A.: Implementing ChatBots using Neural Machine Translation Techniques (2018). Universitat Politècnica de CatalunyaGoogle Scholar
  24. 24.
    Csáky, R.: Deep Learning Based Chatbot Models (2017).
  25. 25.
    Pelk, H.: Machine learning, neural networks and algorithms (2016). (cited 18 January 2019).
  26. 26.
    Wikipedia contributors. Artificial neural network. In: Wikipedia, The Free Encyclopedia. Accessed 13:24, 25 Jan 2019
  27. 27.
    Young, T., et al.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55–75 (2018)CrossRefGoogle Scholar
  28. 28.
    Cahn, J.: CHATBOT: Architecture, design, and development. In: University of Pennsylvania School of Engineering and Applied Science Department of Computer and Information Science (2017)Google Scholar
  29. 29.
    Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014). arXiv preprint arXiv:1406.1078
  30. 30.
    Sak, H., Senior, A., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Fifteenth Annual Conference of the International Speech Communication Association (2014)Google Scholar
  31. 31.
    Ji, Z., et al.: An information retrieval approach to short text conversation (2014). arXiv preprint. arXiv:1408.6988.1Google Scholar
  32. 32.
    Duijst, D., Sandberg, J., Buzzo, D.: Can we Improve the User Experience of Chatbots with Personalisation? (2017)Google Scholar
  33. 33.
    Liu, B., Xu, Z., Sun, C., Wang, B., Wang, X., Wong, D.F., Zhang, M.: Content-oriented user modeling for personalized response ranking in Chatbots. IEEE/ACM Trans. Audio Speech Lang. Process. (2018)Google Scholar
  34. 34.
    Pilato, G., Augello, A., Gaglio, S.: A modular architecture for adaptive ChatBots. In: Proceedings of 5th IEEE International Conference on Semantic Computing, ICSC 2011 (2011)Google Scholar
  35. 35.
    Vtyurina, A., Savenkov, D., Agichtein, E., Clarke, C.L.A.: Exploring conversational search with humans, assistants, and wizards. In: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems – CHI EA 2017 (2017)Google Scholar
  36. 36.
    Niculescu, A.I., Banchs, R.E.: Strategies to cope with errors in human-machine spoken interactions: using chatbots as back-off mechanism for task-oriented dialogues. In: Errors by Humans Mach. multimedia, multimodal Multiling, Data Process (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shafquat Hussain
    • 1
    Email author
  • Omid Ameri Sianaki
    • 1
  • Nedal Ababneh
    • 1
  1. 1.College of Engineering and Science, Victoria University SydneySydneyAustralia

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