Automatic domain modeling for human–robot interaction


This paper introduces an approach to automatic domain modeling for human–robot interaction. The proposed approach is symbolic and intended for semantically unconstrained task-oriented human–robot interaction domains. At the specification level, it is cognitively inspired, addressing selected cognitive mechanisms of the human memory system (e.g., integration, semantic categorization, associative learning, etc.) that are relevant for natural language human–robot interaction. We discuss a corpus-based validation of the introduced approach and report on its particular implementation within the conversational agent integrated with a human-like robot.

This is a preview of subscription content, access via your institution.

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  1. 1.

    Anderson JR (2007) How can the human mind occur in the physical universe?. Oxford University Press, Oxford

    Book  Google Scholar 

  2. 2.

    Anderson JR (1996) ACT: a simple theory of complex cognition. Am Psychol 51(4):355–365

    MathSciNet  Article  Google Scholar 

  3. 3.

    Anderson JR (1985) Cognitive psychology and its implications, 2nd edn. Freeman, New York

    Google Scholar 

  4. 4.

    Anderson JR (1983) The architecture of cognition. Harvard University Press, Cambridge

    Google Scholar 

  5. 5.

    Anderson JR, Bower GH (1973) Human associative memory. Wiley, New York

    Google Scholar 

  6. 6.

    Bothell D (2007) ACT-R 6.0 Reference Manual. From the ACT-R Web site: Accessed 06 Jun 2019

  7. 7.

    Collins AM, Loftus EF (1975) A spreading activation theory of semantic memory. Psychol Rev 82(6):407–428

    Article  Google Scholar 

  8. 8.

    Di Nuovo A, Broz F, Wang N, Belpaeme T, Cangelosi A, Jones R, Esposito R, Cavallo F, Dario P (2018) The multi-modal interface of Robot-Era multi-robot services tailored for the elderly. Intell Serv Robot 11(1):109–126

    Article  Google Scholar 

  9. 9.

    Fillmore CJ (1968) The case for case. In: Bach E, Harms R (eds) Universals in linguistic theory. Holt, Rinehart, and Winston, New York, NY, pp 1–89

  10. 10.

    Firth J (1968) A synopsis of linguistic theory 1930–1955. Studies in linguistic analysis. In: Palmer FR (ed), Selected Papers of J. R. Firth (1952–59). Longmans, London, pp 168–205

  11. 11.

    Gnjatović M (2014) Therapist-centered design of a robot’s dialogue behavior. Cogn Comput 6(4):775–788

    Article  Google Scholar 

  12. 12.

    Gnjatović M, Delić V (2014) Cognitively-inspired representational approach to meaning in machine dialogue. Knowl Based Syst 71:25–33

    Article  Google Scholar 

  13. 13.

    Gnjatović M, Delić V (2013) Electrophysiologically-inspired evaluation of dialogue act complexity. In: Proceedings of the 4th IEEE International Conference on Cognitive Infocommunications, CogInfoCom, Budapest, Hungary, pp 167–72

  14. 14.

    Gnjatović M, Janev M, Delić V (2012) Focus tree: modeling attentional information in task-oriented human–machine interaction. Appl Intell 37(3):305–320

    Article  Google Scholar 

  15. 15.

    Gnjatović M, Rösner D (2010) Inducing genuine emotions in simulated speech-based human–machine interaction: the NIMITEK corpus. IEEE Trans Affect Comput 1(2):132–144

    Article  Google Scholar 

  16. 16.

    Halliday MAK (2004) An introduction to functional grammar, 3rd edn. Hodder Arnold, London

    Google Scholar 

  17. 17.

    Harris Z (1954) Distributional structure. Word 10(23):146–162

    Article  Google Scholar 

  18. 18.

    Hernández-García D, Monje C, Balaguer C (2016) A use case of an adaptive cognitive architecture for the operation of humanoid robots in real environments. Int J Adv Robot Syst 14(1):1–15

    Google Scholar 

  19. 19.

    Johnson TR, Wang H, Zhang J, Wang Y (2002) A model of spatio-temporal coding of memory for multidimensional stimuli. In: Proceedings of the 24th annual meeting of the cognitive science society, Fairfax, VA: August

  20. 20.

    Jokinen K, McTear M (2009) Spoken Dialogue Systems. Synthesis lectures on human language technologies. Morgan and Claypool vol 2, No. 1, pp 1–151

  21. 21.

    Jones MN, Mewhort DJK (2007) Representing word meaning and order information in a composite holographic lexicon. Psychol Rev 114(1):1–37

    Article  Google Scholar 

  22. 22.

    Jurafsky D, Martin JH (2009) Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd edn. Prentice-Hall, Upper Saddle River

    Google Scholar 

  23. 23.

    Landauer T, Dumais S (1997) A solution to Plato’s problem: the latent semantic analysis theory of the acquisition, induction, and representation of knowledge. Psychol Rev 104(2):211–240

    Article  Google Scholar 

  24. 24.

    Lund K, Burgess C (1996) Producing high-dimensional semantic spaces from lexical co-occurrence. Behav Res Methods Instrum Comput 28(2):203–208

    Article  Google Scholar 

  25. 25.

    Mišković D, Gnjatović M, Štrbac P, Trenkić B, Nikša J, Delić V (2017) Hybrid methodological approach to context-dependent speech recognition. Int J Adv Robot Syst 14(1):1–12

    Article  Google Scholar 

  26. 26.

    Pineda LA, Rodríguez A, Fuentes G, Rascón C, Meza I (2017) A light non-monotonic knowledge-base for service robots. Intell Serv Robot 10(3):159–171

    Article  Google Scholar 

  27. 27.

    Pylyshyn Z (1973) What the mind’s eye tells the mind’s brain: a critique of mental imagery. Psychol Bull 80(1):1–24

    Article  Google Scholar 

  28. 28.

    Quillian MR (1969) The teachable language comprehender: a simulation program and theory of language. Commun ACM 12(8):459–476

    Article  Google Scholar 

  29. 29.

    Quillian MR (1968) Semantic memory. In: Minsky MU (ed) Semantic information processing. MIT Press, Cambridge

    Google Scholar 

  30. 30.

    Rogers TT, McClelland JL (2014) Parallel distributed processing at 25: further explorations in the microstructure of cognition. Cogn Sci 38(6):1024–1077

    Article  Google Scholar 

  31. 31.

    Rumelhart DE, McClelland JL (1986) Parallel distributed processing, explorations in the microstructure of cognition. Volume 1: foundations. MIT Press, Cambridge

    Google Scholar 

  32. 32.

    Rutledge-Taylor M, Lebiere C, Thomson R, Staszewski J, Anderson JR (2012) A comparison of rule-based versus exemplar-based categorization using the ACT-R architecture. In: Proceedings of the 21st annual behavior representation in modeling and simulation conference, pp 44–50

  33. 33.

    Savić S, Gnjatović M, Mišković D, Tasevski J, Maček N (2017) Cognitively-inspired symbolic framework for knowledge representation. In: Proceedings of the 8th IEEE international conference on cognitive Infocommunications (CogInfoCom), Debrecen, Hungary, September 2017, pp 315–320

  34. 34.

    Sternberg RJ, Sternberg K (2012) Cognitive psychology, 6th edn. Cengage Learning, Wadsworth

    Google Scholar 

  35. 35.

    Stowell T (1981) Origins of phrase structure, Ph.D. Thesis. Department of Linguistics and Philosophy, Massachusetts Institute of Technology

  36. 36.

    Tasevski J, Gnjatović M, Borovac B (2018) Assessing the Children’s receptivity to the robot MARKO. Acta Polytech Hung 15(5):47–66

    Google Scholar 

  37. 37.

    Trafton G, Hiatt L, Harrison A, Tamborello F, Khemlani S, Schultz A (2013) ACT-R/E: an embodied cognitive architecture for human–robot interaction. J Hum Robot Interact 2(1):30–55

    Article  Google Scholar 

  38. 38.

    Thomson R, Pyke A, Trafton JG, Hiatt LM (2015) An account of associative learning in memory recall. In: Proceedings of the 37th annual conference of the cognitive science society. Cognitive Science Society. Austin, pp 2386–2391

Download references


The presented study was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia (research Grants III44008 and TR32035), and as part of the project “Collaborative strategies of heterogeneous robot activity at solving agriculture missions controlled via intuitive human–robot interfaces” (ID 99), sponsored within the framework of the ERA.Net RUS Plus program. The responsibility for the content of this article lies with the authors.

Author information



Corresponding author

Correspondence to Milan Gnjatović.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Savić, S.Ž., Gnjatović, M., Stefanović, D. et al. Automatic domain modeling for human–robot interaction. Intel Serv Robotics 13, 99–111 (2020).

Download citation


  • Human–robot interaction
  • Domain modeling
  • Cognitive mechanisms
  • Focus tree
  • Robot MARKO