, Volume 34, Issue 1, pp 9–17 | Cite as

Is it possible to grow an I–Thou relation with an artificial agent? A dialogistic perspective

  • Stefan Trausan-MatuEmail author
Original Article


The paper analyzes if it is possible to grow an I–Thou relation in the sense of Martin Buber with an artificial, conversational agent developed with Natural Language Processing techniques. The requirements for such an agent, the possible approaches for the implementation, and their limitations are discussed. The relation of the achievement of this goal with the Turing test is emphasized. Novel perspectives on the I–Thou and I–It relations are introduced according to the sociocultural paradigm and Mikhail Bakhtin’s dialogism, polyphony inter-animation, and carnavalesque. The polyphonic model, the associated analysis method, and the support tools are introduced. Some ideas on how the polyphonic model may be used for the implementation of a computer application able to analyze some features of the existence of an I–Thou relation are included.


I–Thou Conversational agent Dialogism Polyphonic model Inter-animation Empathy 



I would like to thank to Gerry Stahl for his encouragement in my research toward the development of the polyphonic model and analysis method starting from Bakhtin’s ideas. I also want to thank to the anonymous reviewers for their useful recommendations.


  1. Bakhtin MM (1981) The dialogic imagination: four essays. University of Texas Press, AustinGoogle Scholar
  2. Bakhtin MM (1984a) Problems of Dostoevsky’s poetics, theory and history of literature series, vol. 8 (edited and translated by C. Emerson). ​University of Minnesota Press, MinneapolisGoogle Scholar
  3. Bakhtin MM (1984b) Rabelais and his world. Indiana University Press, BloomingtonGoogle Scholar
  4. Bakhtin MM (1986) Speech genres and other late essays. University of Texas Press, AustinGoogle Scholar
  5. Buber M (1970) I and Thou, translation Walter Kaufmann. Charles Scribner’s Sons, New YorkGoogle Scholar
  6. Clark K, Holquist JM (1984) Mikhail Bakhtin. Harvard University Press, CambridgeGoogle Scholar
  7. Crowell S (2015) “Existentialism”, The Stanford Encyclopedia of Philosophy (Spring 2015 Edition). In: Zalta EN (ed).
  8. Dascalu M, Dessus P, Trausan-Matu S, Bianco M, Nardy A (2013) ReaderBench, an environment for analyzing text complexity and reading strategies. In: Lane HC, Yacef K, Mostow J, Pavlik P (eds) 16th Int. Conf. on artificial intelligence in education (AIED 2013). Springer, Memphis, pp 379–388Google Scholar
  9. Fromm E (1955) The sane society. Fawcett Premier, Greenwich, ConnecticutGoogle Scholar
  10. Hovy EH (2015) What are sentiment, affect, and emotion? Applying the methodology of Michael Zock to sentiment analysis. In: Gala N et al (eds) Language production, cognition, and the lexicon, text, speech and language technology 48. Springer International Publishing, SwitzerlandGoogle Scholar
  11. Jurafsky D, Martin JH (2009) Speech and language processing. In: An introduction to natural language processing, computational linguistics, and speech recognition, 2nd edn. Pearson Prentice Hall, LondonGoogle Scholar
  12. Koffka K (1935) Principles of gestalt psychology. Harcourt, Brace, New YorkGoogle Scholar
  13. Lakoff G, Johnson M (1980) Metaphors we live by. University of Chicago Press, ChicagoGoogle Scholar
  14. Levinas E (1979) Totality and infinity. Translated by Alphonso Lingis. Duquesne University Press, PittsburghGoogle Scholar
  15. Marcus S (1997) Empatie si personalitate, Ed. Atos, Bucharest (in Romanian)Google Scholar
  16. Mihailovic A (1997) Corporeal words: Mihail Bakhtin’s theology of discourse. Northwestern University Press, ChicagoGoogle Scholar
  17. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retriev 2:1–135CrossRefGoogle Scholar
  18. Schubert LK (2015) What kinds of knowledge are needed for genuine understanding? IJCAI 2015 workshop on cognitive knowledge acquisition and applications (Cognitum 2015), Buenos AiresGoogle Scholar
  19. Stahl G (2006) Group cognition. Computer support for building collaborative knowledge. MIT Press, CambridgeCrossRefGoogle Scholar
  20. Takahashi H, Horii T, Endo N, Morita T, Yokoyama H, Asada M (2014) How does emphatic emotion emerge via human–robot rhythmic interaction?, HAI 2014, ACM, Tsukuba, JapanGoogle Scholar
  21. Tannen D (1989) Talking voices: repetition, dialogue, and imagery in conversational discourse. Cambridge University Press, New YorkGoogle Scholar
  22. Thompson E (2007) Mind in life: biology, phenomenology, and the sciences of mind. Harvard University Press, LondonGoogle Scholar
  23. Trausan-Matu S (2010a) Computer support for creativity in small groups using chats. Ann Acad Rom Sci Ser Sci Technol Inf 3(2):81–90Google Scholar
  24. Trausan-Matu S (2010b) The polyphonic model of hybrid and collaborative learning. In: Wang FL, Fong J, Kwan RC (eds.) Handbook of research on hybrid learning models: advanced tools, technologies, and applications. Information Science Publishing, Hershey, pp 466–486CrossRefGoogle Scholar
  25. Trausan-Matu S (2012) Repetition as artifact generation in polyphonic CSCL chats. In: Third international conference on emerging intelligent data and web technologies. IEEE, Bucharest, pp 194–198Google Scholar
  26. Trausan-Matu S (2013) Collaborative and differential utterances, pivotal moments, and polyphony. In: Suthers D, Lund K, Rosé CP, Law N (eds) Productive multivocality. Springer, New York, pp 123–139CrossRefGoogle Scholar
  27. Trausan-Matu S, Murarus RI (2015) Evaluarea invatarii colaborative pe chat, pe baza analizei repetitiilor si altruismului, Revista Romana de Interactiune Om-Calculator, 8(3), pp 223–236Google Scholar
  28. Trausan-Matu S, Rebedea T (2010) A polyphonic model and system for inter-animation analysis in chat conversations with multiple participants. In: Gelbukh AF (ed) 11th international conference on computational linguistics and intelligent text processing (CICLing 2010). Springer, Iasi, pp 354–363CrossRefGoogle Scholar
  29. Trausan-Matu S, Rebedea T, Dragan A, Alexandru C (2007a) Visualisation of learners’ contributions in chat conversations. In: Fong J, Wang FL (eds) Blended learning. Pearson/Prentice Hall, Singapour, pp 217–226Google Scholar
  30. Trausan-Matu S, Stahl G, Sarmiento J (2007b) Supporting polyphonic collaborative learning. E-service J, vol. 6, nr. 1, Indiana University Press, pp 58–74Google Scholar
  31. Trausan-Matu S, Dascalu M, Rebedea T (2014) PolyCAFe—automatic support for the polyphonic analysis of CSCL chats Int J Comput Support Collab Learn, Springer 9:127–156CrossRefGoogle Scholar
  32. Turing AM (1950) Computing machinery and intelligence Mind, 59, pp 433–460MathSciNetCrossRefGoogle Scholar
  33. Voloshinov V (1973) Marxism and the philosophy of language. Seminar Press, New YorkGoogle Scholar
  34. Vygotsky L (1978) Mind in society. Harvard University Press, Cambridge, MAGoogle Scholar
  35. Weizenbaum J (1966) ELIZA—a computer program for the study of natural language communication between man and machine. Commun ACM 9(1):36–45CrossRefGoogle Scholar
  36. Winograd T (1987) Thinking machines: can there be? Are we?, Report No. STAN-CS-87-1161, StanfordGoogle Scholar

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity Politehnica of BucharestBucharestRomania
  2. 2.Romanian Academy Research Institute for Artificial IntelligenceBucharestRomania

Personalised recommendations