Skip to main content

Learning from Interaction: An Intelligent Networked-Based Human-Bot and Bot-Bot Chatbot System

  • Conference paper
  • First Online:
Advances in Computational Intelligence Systems (UKCI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 840))

Included in the following conference series:

Abstract

In this paper we propose an approach to a chatbot software that is able to learn from interaction via text messaging between human-bot and bot-bot. The bot listens to a user and decides whether or not it knows how to reply to the message accurately based on current knowledge, otherwise it will set about to learn a meaningful response to the message through pattern matching based on its previous experience. Similar methods are used to detect offensive messages, and are proved to be effective at overcoming the issues that other chatbots have experienced in the open domain. A philosophy of giving preference to too much censorship rather than too little is employed given the failure of Microsoft Tay. In this work, a layered approach is devised to conduct each process, and leave the architecture open to improvement with more advanced methods in the future. Preliminary results show an improvement over time in which the bot learns more responses. A novel approach of message simplification is added to the bot’s architecture, the results suggest that the algorithm has a substantial improvement on the bot’s conversational performance at a factor of three.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /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
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Kuligowska, K.: Commercial chatbot: performance evaluation, usability metrics and quality standards of embodied conversational agents (2015)

    Google Scholar 

  2. Alexa Amazon: “Amazon” (2014)

    Google Scholar 

  3. Shawar, B.A., Atwell, E.: Machine learning from dialogue corpora to generate chatbots. Expert Update J. 6(3), 25–29 (2003)

    Google Scholar 

  4. Jia, J.: The study of the application of a web-based chatbot system on the teaching of foreign languages. In: Society for Information Technology & Teacher Education International Conference. Association for the Advancement of Computing in Education (AACE), pp. 1201–1207 (2004)

    Google Scholar 

  5. Heller, B., Proctor, M., Mah, D., Jewell, L., Cheung, B.: Freudbot: an investigation of chatbot technology in distance education. In: EdMedia: World Conference on Educational Media and Technology, pp. 3913–3918. Association for the Advancement of Computing in Education (AACE) (2005)

    Google Scholar 

  6. Tatai, G., Csordás, A., Kiss, Á., Szaló, A., Laufer, L.: Happy chatbot, happy user. In: Intelligent Virtual Agents, pp. 5–12. Springer, Heidelberg (2003)

    Google Scholar 

  7. Mauldin, M.L.: Chatterbots, tinymuds, and the turing test: entering the loebner prize competition. In: AAAI, vol. 94, pp. 16–21 (1994)

    Google Scholar 

  8. Shawar, B.A., Atwell, E.: Different measurements metrics to evaluate a chatbot system. In: Proceedings of the Workshop on Bridging the Gap: Academic and Industrial Research in Dialog Technologies, pp. 89–96. Association for Computational Linguistics (2007)

    Google Scholar 

  9. Wallace, R.: Artificial linguistic internet computer entity (ALICE) (2001). https://www.chatbots.org/chatbot/a.l.i.c.e/. Accessed 25 May 2018

  10. The Exeter Blog: The Loebner Prize, a Turing Test competition at Bletchley Park (2014). https://blogs.exeter.ac.uk/exeterblog/blog/2014/12/08/the-loebner-prize-a-turing-test-competition-at-bletchley-park/

  11. Microsoft (March). Tay AI. https://twitter.com/tayandyou. Accessed 25 May 2018

  12. Wakefield, J.: BBC News. Microsoft chatbot is taught to swear on Twitter (2016). http://www.bbc.co.uk/news/technology-35890188. Accessed 12 Apr 2018

  13. Google Cloud Products (n.d.), 28 March 2018. https://cloud.google.com. Accessed 25 May 2018

  14. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  15. Haridy, R.: Microsoft’s speech recognition system is now as good as a human (2017). https://newatlas.com/microsoft-speech-recognition-equals-humans/50999/. Accessed 6 Apr 2018

  16. AllSlang. (n.d.). Swear Word List, Dictionary, Filter, and API. https://www.noswearing.com/. Accessed 11 Mar 2018

  17. Faria, D.R., Vieira, M., Faria, F.C.C., Premebida, C.: Affective facial expressions recognition for human-robot interaction. In: IEEE International Symposium on Robot and Human Interactive Communication, pp. 805–810 (2017)

    Google Scholar 

  18. Faria, D.R., Vieira, M., Faria, F.C.C.: Towards the development of affective facial expression recognition for human-robot interaction. In: International Conference on Pervasive Technologies Related to Assistive Environments, pp. 300–304 (2017)

    Google Scholar 

  19. Bertero, D., Siddique, F., Wu, C., Wan, Y., Chan, R., Fung, P.: Real-time speech emotion and sentiment recognition for interactive dialogue systems. In: Conference on Empirical Methods in Natural Language Processing, pp. 1042–1047 (2016)

    Google Scholar 

  20. Vieira, M., Faria, D.R., Nunes, U.: Real-time application for monitoring human daily activities and risk situations in robot-assisted living. In: 2nd Iberian Robotics Conference, pp. 449–461 (2015)

    Google Scholar 

  21. Turing, A.M.: Computing machinery and intelligence. Mind 49, 433–460 (1950)

    Article  MathSciNet  Google Scholar 

  22. Weizenbaum, J.: Computer Power and Human Reason: From Judgment to Calculation. W.H. Freeman and Company, New York (1976)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Diego R. Faria .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bird, J.J., Ekárt, A., Faria, D.R. (2019). Learning from Interaction: An Intelligent Networked-Based Human-Bot and Bot-Bot Chatbot System. In: Lotfi, A., Bouchachia, H., Gegov, A., Langensiepen, C., McGinnity, M. (eds) Advances in Computational Intelligence Systems. UKCI 2018. Advances in Intelligent Systems and Computing, vol 840. Springer, Cham. https://doi.org/10.1007/978-3-319-97982-3_15

Download citation

Publish with us

Policies and ethics