Leveraging Machinima to Characterize Comprehension of Character Motivation

  • Kara Cassell
  • R. Michael YoungEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11869)


Deliberation-driven reflective sequences, or DDRSs, are cinematic idioms used by film makers to convey the motivations for characters adopting a particular course of action in a story. We report on an experiment where the cinematic generation system Ember was used to create a cinematic sequence with variants making different choices for DDRS use around a single decision point for a single character.


Experimental evaluation Cinematic generation Machinima 


  1. 1.
    Arijon, D.: Grammar of the Film Language. Silman-James Press (1976)Google Scholar
  2. 2.
    Bares, W., McDermott, S., Boudreaux, C., Thainimit, S.: Virtual 3D camera composition from frame constraints. In: Proceedings of the Eighth ACM International Conference on Multimedia, pp. 177–186 (2000)Google Scholar
  3. 3.
    Cassell, B.A., Young, R.M.: Ember, toward salience-based cinematic generation. In: Workshop on Intelligent Narrative Technologies at the Ninth Annual AAAI Conference of Artificial Intelligence and Interactive Digital Entertainment (2013)Google Scholar
  4. 4.
    Cassell, K.: Dynamic Generation of Narrative Discourse that Communicates Character Decision Making. North Carolina State University (2019)Google Scholar
  5. 5.
    Cheong, Y.G., Jhala, A., Bae, B.C., Young, R.M.: Automatically generating summary visualizations from game logs. In: AIIDE, pp. 167–172 (2008)Google Scholar
  6. 6.
    Christianson, D.B., Anderson, S.E., He, L.W., Salesin, D.H., Weld, D.S., Cohen, M.F.: Declarative camera control for automatic cinematography. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence and the Eighth Innovative Applications of Artificial Intelligence Conference, pp. 148–155 (1996)Google Scholar
  7. 7.
    Christie, M., Normand, J.M.: A semantic space partitioning approach to virtual camera composition. Comput. Graph. Forum 24(3), 247–256 (2005)CrossRefGoogle Scholar
  8. 8.
    Elson, D.K., Riedl, M.O.: A lightweight intelligent virtual cinematography system for machinima production. In: Artificial Intelligence and Interactive Digital Entertainment (2007)Google Scholar
  9. 9.
    Jhala, A., Young, R.M.: Cinematic visual discourse: representation, generation, and evaluation. IEEE Trans. Comput. Intell. AI Games 2(2), 69–81 (2010)CrossRefGoogle Scholar
  10. 10.
    Lino, C.: Virtual camera control using dynamic spatial partitions. Ph.D. thesis, University Rennes 1 (2013)Google Scholar
  11. 11.
    Monaco, J.: How to Read a Film: The Art, Technology, Language, History, and Theory of Film and Media. Oxford University Press, New York (1981)Google Scholar
  12. 12.
    Moore, J.D., Paris, C.L.: Planning text for advisory dialogues: capturing intentional and rhetorical information. Comput. Linguist. 19(4), 651–694 (1993)Google Scholar
  13. 13.
    Tomlinson, B., Blumberg, B., Nain, D.: Expressive autonomous cinematography for interactive virtual environments. In: Proceedings of the Fourth International Conference on Autonomous Agents, pp. 317–324. ACM (2000)Google Scholar
  14. 14.
    Wu, H.Y., Palù, F., Ranon, R., Christie, M.: Thinking like a director: film editing patterns for virtual cinematographic storytelling. ACM Trans. Multimedia Comput. Commun. Appl. 14(4), 81:1–81:22 (Oct 2018).,
  15. 15.
    Young, R.M., Pollack, M.E., Moore, J.D.: Decomposition and causality in partial order planning. In: Proceedings of the Second International Conference on AI and Planning Systems, pp. 188–193 (1994)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.3C InstituteDurhamUSA
  2. 2.University of UtahSalt Lake CityUSA

Personalised recommendations