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Qualifying and Quantifying Interestingness in Dramatic Situations

  • Nicolas Szilas
  • Sergio Estupiñán
  • Urs Richle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10045)

Abstract

Dramatic situations have long been studied in Drama Studies since they characterize tension and interestingness in a plot. In the field of Interactive Digital Storytelling (IDS), integrating knowledge about dramatic situations is of great relevance in order to design improved systems that dynamically generate more narratively-relevant events. However, current approaches to dramatic situations are descriptive and not directly applicable to the field of IDS. We introduce a computational model that fills that gap by both describing dramatic situations visually and providing a quantitative measure for the interestingness of a plot. Using a corpus of 20 Aesop’s fables, we compared the calculations resulting of the model with the assessments provided by 101 participants. Results suggest that our model works appropriately at least for stories characterized by a strong plot structure rather than their semantic content.

Keywords

Interactive storytelling Interactive narrative Interactive drama Computational narratology Computational models of narrative Dramatic situation Aesop’s fables 

Notes

Acknowledgment

This research was made possible thanks to the support of the Swiss National Science Foundation under grant #159605 - Fine-grained Evaluation of the Interactive Narrative Experience.

References

  1. 1.
    Aesop: Aesop’s Fables. Wordsworth Classics (1994)Google Scholar
  2. 2.
    Barber, H., Kudenko, D.: Dynamic generation of dilemma-based interactive narratives. In: Proceedings of Third Conference on Artificial Intelligence and Interactive Digital Entertainment – AIIDE, pp. 2–7. AAAI Press, Menlo Park, CA (2007)Google Scholar
  3. 3.
    Battaglino, C., Damiano, R., Torino, U.: A character model with moral emotions : preliminary evaluation. In: Finlayson, M.A., Meister, J.C., Bruneau, E.G. (eds.) 5th Workshop on Computational Models of Narrative (CMN 2014), pp. 24–41. OASICS (2014)Google Scholar
  4. 4.
    Elson, D.K.: Detecting story analogies from annotations of time, action and agency. In: LREC 2012 Workshop on Computational Models of Narrative, pp. 91–99 (2012)Google Scholar
  5. 5.
    Elson, D.K.: DramaBank: annotating agency in narrative discourse. In: Proceedings of the Eigth International Conference on Language Resources and Evaluation (LREC), pp. 2813–2819 (2012)Google Scholar
  6. 6.
  7. 7.
    Lavandier, Y.: La Dramaturgie. Le clown et l’enfant, Cergy (1997)Google Scholar
  8. 8.
    Lehnert, W.: Plot units and narrative summarization. Cogn. Sci. 5(4), 293–331 (1981)CrossRefGoogle Scholar
  9. 9.
    Levi-Strauss, C.: Anthropologie Structurale. Plon, Paris (1958)Google Scholar
  10. 10.
    Lombardo, V., Battaglino, C., Pizzo, A., Damiano, R., Lieto, A.: Coupling conceptual modeling and rules for the annotation of dramatic media. Semant. Web. 6(5), 503–534 (2015)CrossRefGoogle Scholar
  11. 11.
    McKee, R.: Story: Substance, Structure, Style, and the Principles of Screenwriting. Harper Collins, New York (1997)Google Scholar
  12. 12.
    Nichols, B.: Ideology and the Image. Indiana University Press, Bloomington (1981)Google Scholar
  13. 13.
    Nünning, A., Sommer, R.: Diegetic and mimetic narrativity: some further steps towards a narratology of drama. In: Pier, J., Landa, J.Á.G. (eds.) Theorizing Narrativity, vol. 12, pp. 331–354. Walter de Gruyter, Berlin, New York (2008)Google Scholar
  14. 14.
    Pavis, P.: Dictionary of the Theatre: Terms, Concepts and Analysis, trans. Christine Shantz. University of Toronto Press, Toronto, Buffalo (1998)Google Scholar
  15. 15.
    Polti, G.: Les Trente-six Situations Dramatiques. Mercure de France, Paris (1903)Google Scholar
  16. 16.
    Russel, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs (1995)MATHGoogle Scholar
  17. 17.
    Sgouros, N.: Dynamic generation, management and resolution of interactive plots. Artif. Intell. 107(1), 29–62 (1999)CrossRefMATHGoogle Scholar
  18. 18.
    Souriau, E.: Les Deux Cent Mille Situations Dramatiques. Flammarion, Paris (1950)Google Scholar
  19. 19.
    Struck, H.-G.: Telling stories knowing nothing: tackling the lack of common sense knowledge in story generation systems. In: Subsol, G. (ed.) ICVS 2005. LNCS, vol. 3805, pp. 189–198. Springer, Heidelberg (2005). doi: 10.1007/11590361_22 CrossRefGoogle Scholar
  20. 20.
    Szilas, N.: A computational model of an intelligent narrator for interactive narratives. Appl. Artif. Intell. 21(8), 753–801 (2007)CrossRefGoogle Scholar
  21. 21.
    Szilas, N.: IDtension: a narrative engine for Interactive Drama. In: Göbel, S., Braun, N., Spierling, U., Dechau, J., Diener, H. (eds.) Proceedings of the Technologies for Interactive Digital Storytelling and Entertainment (TIDSE) Conference, pp. 187–203. Fraunhofer IRB, Darmstadt (2003)Google Scholar
  22. 22.
    Szilas, N.: Modeling and representing dramatic situations as paradoxical structures. In: Digital Scholarship in the Humanities (2016)Google Scholar
  23. 23.
    Szilas, N.: Structural models for Interactive Drama. In: 2nd International Conference on Computational Semiotics for Games and New Media (COSIGN) (2002)Google Scholar
  24. 24.
    Vale, E.: The Technique of Screenplay Writing. Grosset & Dunlap, New York (1973)Google Scholar
  25. 25.
    Ware, S.G., Young, R.M.: CPOCL: a narrative planner supporting conflict. In: Proceedings of the Seventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 97–102. AAAI Press, Palo Alto, CA (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Nicolas Szilas
    • 1
  • Sergio Estupiñán
    • 1
  • Urs Richle
    • 1
  1. 1.TECFA, FPSEUniversity of GenevaGenève 4Switzerland

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