Machine Imagination: A Step Toward the Construction of Artistic World Through Storytelling

  • Syed Tanweer Shah Bukhari
  • Asma Kanwal
  • Wajahat Mahmood QaziEmail author
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


Development of conscious machines requires the implementation of artifacts that might enable an agent to formulate, represent, and regulate its actions and behaviors in a diverse environment. Regulation involves the rehearsal of all context-based possibilities considering emotional state and goals before execution of motor actions. It also means that an agent should be able to manipulate information that is not directly perceived through sensory stimuli. In order to incorporate these abilities, an agent is required to be constructed with cognitive memories, sensory system, sensory integration, emotion, drives, motivations, and action regulatory system. This study proposes a sub-architecture for QuBIC (cognitive architecture) to generate imaginations in QuBIC agents. The proposed architecture was implemented along with required computational constructs discussed in the paper. Furthermore, the paper presents the empirical analysis showing the potential of the proposed solution to construct imaginations in an agent. Experimental results illustrate how the aforementioned cognitive states participated in the process of machine imagination.


Machine consciousness Imaginations Cognitive robots 


  1. 1.
    Karwowski, M., Jankowska, D. M., & Szwajkowski, W. (2016). Creativity, imagination, and early mathematics education. In R. Leikin & B. Sriraman (Eds.), Creativity and Giftedness (pp. 7–22). Berlin: Springer.Google Scholar
  2. 2.
    Moreton, J., Callan, M. J., & Hughes, G. (2017). How much does emotional valence of action outcomes affect temporal binding? Consciousness and Cognition, 49, 25–34.CrossRefGoogle Scholar
  3. 3.
    Hunter, M. (2013). Imagination may be more important than knowledge: The eight types of imagination we use. Review of Contemporary Philosophy, 12, 113–120.Google Scholar
  4. 4.
    Shanahan, A. (2005). Consciousness, emotion, and imagination: A brain-inspired architecture for cognitive robotics. In Proceedings AISB 2005 Symposium on Next Generation Approaches to Machine Consciousness.Google Scholar
  5. 5.
    Kneller, J. (2007). Kant and the power of imagination (1st ed.). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  6. 6.
    Gibson, J. J. (1986). The ecological approach to visual perception. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  7. 7.
    Goslee, N. M. (2014). Shelley’s visual imagination. Cambridge: Cambridge University Press.Google Scholar
  8. 8.
    Wittgenstein, L. (2001). Philosophical investigations. Hoboken, NJ: Wiley.zbMATHGoogle Scholar
  9. 9.
    Taylor, M. (2011). Encyclopedia of creativity—imagination (S. P. Mark Runco, Ed.). New York: Elsevier Inc.Google Scholar
  10. 10.
    Faghihi, U., McCall, R., & Franklin, S. (2012). A computational model of attentional learning in a cognitive agent. Biologically Inspired Cognitive Architectures, 2, 25–36.CrossRefGoogle Scholar
  11. 11.
    Aleksander, I., & Dunmall, B. (2003). Axioms and tests for the presence of minimal consciousness in agents. Journal of Consciousness Studies, 10, 7–18.Google Scholar
  12. 12.
    Haikonen, P. O. (2003). The Cognitive Approach to Conscious Machines. Exeter: Imprint Academic.Google Scholar
  13. 13.
    Haikonen, P. O. (2005). You only live twice: Imagination in conscious machines. In Symposium on Next Generation approaches to Machine Consciousness: Imagination, Development, Inter-subjectivity, and Embodiment.Google Scholar
  14. 14.
    Aleksander, I., & Morton, H. (2007). Why axiomatic models of being conscious? Journal of Consciousness Studies, 14(7), 15–27.Google Scholar
  15. 15.
    Michel, M. (2017). A role for the anterior insular cortex in the global neuronal workspace model of consciousness. Consciousness and Cognition, 49, 333–346.CrossRefGoogle Scholar
  16. 16.
    Aleksander, I. (2001). How to build a mind: Towards machines with imagination. New York: Columbia University Press.Google Scholar
  17. 17.
    Marques, H. G., Holland, O., & Newcombe, R. (2008). A modelling framework for functional imagination. In AISB Convention of Computing & Philosophy.Google Scholar
  18. 18.
    Madl, T., Franklin, S., Chena, K., Montaldid, D., & Trappl, R. (2016). Towards real-world capable spatial memory in the LIDA cognitive architecture. Biologically Inspired Cognitive Architectures, 16, 87–104.CrossRefGoogle Scholar
  19. 19.
    Chalmers, D. J. (1995). The puzzle of conscious experience. Scientific American, 273, 80–86.CrossRefGoogle Scholar
  20. 20.
    Qazi, W. M. (2011). Modeling cognitive cybernetics from unified theory of mind using quantum neuro-computing for machine consciousness.Punjab, Pakistan: National College of Business Administration and Economics.Google Scholar
  21. 21.
    Mel, B. (1986). A connectionist learning model for 3-d mental rotation, zoom, an pan. In Proceedings of Eighth Annual Conference of the Cognitive Science Society.Google Scholar
  22. 22.
    Mel, B. (1988). Murphy: A robot that learns by doing. In Neural information processing systems. New York: American Institute of Physics.Google Scholar
  23. 23.
    Stein, L. A. (1995). Imagination and situated cognition. In Android epistemology (pp. 167–182). Cambridge, MA: MIT Artificial Intelligence Lab.Google Scholar
  24. 24.
    Matarić, M. J. (1990). A distributed model for mobile robot environment-learning and navigation (MIT AI Lab Tech Report AITR-1228).Google Scholar
  25. 25.
    Aleksander, I., Evans, R. G., & Sales, N. (1995). Towards intentional neural systems: Experiments with MAGNUS. In Fourth International Conference on Artificial Neural Networks, Cambridge, UK.Google Scholar
  26. 26.
    Hsiao, K.-Y., Mavridis, N., & Roy, D. (2003). Coupling perception and simulation: Steps towards conversational robotics. In IEEE/RSJ International Conference on Intelligent Robots and Systems 2003.Google Scholar
  27. 27.
    Roy, D., Hsiao, K.-Y., & Mavridis, N. (2003). Conversational robots: Building blocks for grounding word meanings. In Workshop on Learning Word Meaning from Non-Linguistic Data.Google Scholar
  28. 28.
    Roy, D., Hsiao, K.-Y., Mavridis, N., & Gorniak, P. (2003). Ripley, hand me the cup: Sensorimotor representations for grounding word meaning. In International Conference of Automatic Speech Recognition and Understanding.Google Scholar
  29. 29.
    Mavridis, N., & Roy, D. (2006). Grounded situation models for robots: Where words and percepts meet. In IEEE/RSJ International Conference on Intelligent Robots and Systems Google Scholar
  30. 30.
    Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Google Scholar
  31. 31.
    Roy, D., Hsiao, K.-Y., & Mavridis, N. (2004). Mental imagery for a conversational robot. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(3), 1374–1383.CrossRefGoogle Scholar
  32. 32.
    Gamez, D. (2008). The development and analysis of conscious machines. Colchester: University of Essex.Google Scholar
  33. 33.
    Gamez, D. (2008). Progress in machine consciousness. Consciousness and Cognition, 17, 887–910.CrossRefGoogle Scholar
  34. 34.
    Marques, H. G. (2009). Architectures for embodied imagination. Colchester: University of Essex.Google Scholar
  35. 35.
    Potkonjak, V., Svetozarevic, B., Jovanovic, K., & Holland, O. (2012). The puller-follower control of compliant and noncompliant antagonistic tendon drives in robotic systems. International Journal of Advanced Robotic Systems, 8(5), 143–155.Google Scholar
  36. 36.
    Jovanovic, K., Potkonjak, V., & Holland, O. (2014). Dynamic modeling of an anthropomimetic robot in contact tasks. Advanced Robotics, 28(11), 793–806.Google Scholar
  37. 37.
    Baars, B. J. (1997). In the theatre of consciousness: Global workspace theory, a rigorous scientific theory of consciousness. Journal of Consciousness Studies, 4(4), 292–309.Google Scholar
  38. 38.
    Franklin, S., Madl, T., D’Mello, S., & Snaider, J. (2013). LIDA: A systems-level architecture for cognition, emotion, and learning. Autonomous Mental Development, IEEE Transactions, 6(1), 19–41.CrossRefGoogle Scholar
  39. 39.
    Franklin, S., Madl, T., Chen, K., & Trappl, R. (2013). Spatial working memory in the LIDA cognitive architecture. In International Conference on Cognitive Modeling, Ottawa, Canada.Google Scholar
  40. 40.
    Franklin, S. (1997). Artificial minds. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
  41. 41.
    Franklin, S., Madl, T., Strain, S., Faghihi, U., Dong, D., Kugele, S., et al. (2016). A LIDA cognitive model tutorial. Biologically Inspired Cognitive Architectures, 16, 105–130.CrossRefGoogle Scholar
  42. 42.
    Paraense, A. L., Raizer, K., Paula, S. M., Rohmer, E., & Gudwin, R. R. (2016). The cognitive systems toolkit and the CST reference cognitive architecture. Biologically Inspired Cognitive Architectures, 17, 32–48.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Syed Tanweer Shah Bukhari
    • 1
  • Asma Kanwal
    • 2
  • Wajahat Mahmood Qazi
    • 1
    Email author
  1. 1.Intelligent Machines and Robotics, Department of Computer ScienceCOMSATS UniversityIslamabad, Lahore CampusPakistan
  2. 2.Department of Computer ScienceGovernment College UniversityLahorePakistan

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