A Rules-Based System for Adapting and Transforming Existing Narratives

  • Jo MazeikaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10045)


This paper describes a rules-based computational system that utilizes a semantic framework to produce transformations of an existing narrative. We describe how we can use a Rete rules system to transform a semantic representation of a narrative, as well as laying out groundwork for the types of rules that a system like this would consider. To provide an example of our system in action, we describe a semantic encoding of the Brothers Grimm version of Sleeping Beauty, and provide rules for transforming it to fit the style of Disney.


Intelligent narrative technologies Rules system Narrative transformation Narrative representation 


  1. 1.
    Akimoto, T., Ogata, T.: A narratological approach for narrative discourse: implementation and evaluation of the system based on genette and jauss. In: Proceedings of the 34th Annual Conference of the Cognitive Science Society, pp. 1272–1277 (2012)Google Scholar
  2. 2.
    Ashliman, D.L.: Grimm Brothers’ Home Page. Accessed 2 June 2016.
  3. 3.
    Elson, D.K.: Detecting story analogies from annotations of time, action and agency. In: Proceedings of the LREC 2012 Workshop on Computational Models of Narrative, Istanbul, Turkey (2012)Google Scholar
  4. 4.
    Forgy, C.L.: Rete: a fast algorithm for the many pattern/many object pattern match problem. Artif. Intell. 19(1), 17–37 (1982)CrossRefGoogle Scholar
  5. 5.
    Harmon, S.: An expressive dilemma generation model for players and artificial agents. In: Twelfth Artificial Intelligence and Interactive Digital Entertainment Conference (2016)Google Scholar
  6. 6.
    Imabuchi, S., Ogata, T.: Methods for generalizing the propp-based story generation mechanism. In: Yoshida, T., Kou, G., Skowron, A., Cao, J., Hacid, H., Zhong, N. (eds.) AMT 2013. LNCS, vol. 8210, pp. 333–344. Springer, Heidelberg (2013). doi: 10.1007/978-3-319-02750-0_36 CrossRefGoogle Scholar
  7. 7.
    Ogata, T.: Building conceptual dictionaries for an integrated narrative generation system. J. Robot. Netw. Artif. Life 1(4), 270–284 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Riedl, M., Len C.: Generating story analogues. In: AIIDE (2009)Google Scholar
  9. 9.
    Rishes, E., Lukin, S.M., Elson, D.K., Walker, M.A.: Generating different story tellings from semantic representations of narrative. In: Koenitz, H., Sezen, T.I., Ferri, G., Haahr, M., Sezen, D., C̨atak, G. (eds.) ICIDS 2013. LNCS, vol. 8230, pp. 192–204. Springer, Heidelberg (2013). doi: 10.1007/978-3-319-02756-2_24 CrossRefGoogle Scholar
  10. 10.
    Speer, R., Havasi, C.: ConceptNet 5: a large semantic network for relational knowledge. In: Gurevych, I., Kim, J. (eds.) The People’s Web Meets NLP. Theory and Applications of Natural Language Processing, pp. 161–176. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Tearse, B.R., Mawhorter, P.A., Mateas, M., Wardrip-Fruin, N.: Skald: minstrel reconstructed. IEEE Trans. Comput. Intell. AI Game 6, 156–165 (2014)CrossRefGoogle Scholar
  12. 12.
    Theune, M., Slabbers, N., Hielkema, F.: The automatic generation of narratives. In: Proceedings of the 17th Meeting of Computational Linguistics in the Netherlands (CLIN17), pp. 131–146. Leuven, Belgium (2007)Google Scholar
  13. 13.
    Turner, S.: Minstrel: a computer model of creativity and storytelling. Technical report CSD-920057, Ph.D. thesis, Computer Science Department, University of California, Los Angeles, CA (1992)Google Scholar
  14. 14.
    Zhu, J., Ontañón, S.: The sam algorithm for analogy-based story generation. In: Artificial Intelligence, pp. 67–72 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.UC Santa CruzSanta CruzUSA

Personalised recommendations