International Conference on Interactive Digital Storytelling

Interactive Storytelling pp 93-104

Remember That Time? Telling Interesting Stories from Past Interactions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9445)

Abstract

Sociability is a human trait that plays a central part in relationships over time. Today, humans are increasingly in long-term interactions with intelligent agents, which have proven most useful when they are sociable. Such sociability requires the agent to remember and appropriately refer to past interactions. A common way in which humans refer to their past interactions and collaborations is through storytelling. Such stories, often abbreviated, include a small set of interesting and extraordinary events. We propose the design, development and preliminary evaluation of a generic computational architecture for finding and retelling such interesting event sequences. Our system mines interesting interaction episodes in a corpus of prior interactions. Initial evaluation of interactions selected by the system for retelling are encouraging. A future goal of the research is to support collaborative composition of stories about prior interactions between humans and agents in a mixed-initiative framework to produce interesting retellings.

Keywords

Social interaction Storytelling Story generation Human-robot interaction Narrative content selection 

References

  1. 1.
    Allen, N., Templon, J., McNally, P.: Statsmonkey: a data-driven sports narrative writer. In: AAAI Fall Symposium, pp. 2–3 (2010)Google Scholar
  2. 2.
    Behrooz, M., Rich, C., Sidner, C.: On the sociability of a game-playing agent: a software framework and empirical study. In: Bickmore, T., Marsella, S., Sidner, C. (eds.) IVA 2014. LNCS, vol. 8637, pp. 40–53. Springer, Heidelberg (2014) Google Scholar
  3. 3.
    Bickmore, T.W., Picard, R.W.: Establishing and maintaining long-term human-computer relationships. ACM Trans. Comput.-Hum. Interact. 12(2), 293–327 (2005)CrossRefGoogle Scholar
  4. 4.
    Bouayad-Agha, N., Casamayor, G., Wanner, L.: Content selection from an ontology-based knowledge base for the generation of football summaries. In: Proceedings of the 13th European Workshop on Natural Language Generation, pp. 72–81. Association for Computational Linguistics (2011)Google Scholar
  5. 5.
    Buckthal, E., Khosmood, F.: (Re)telling chess stories as game content. In: 9th International Conference on the Foundations of Digital Games (2014). http://fdg2014.org/papers/fdg2014_wip_03.pdf
  6. 6.
    Elson, D.K.: Detecting story analogies from annotations of time, action and agency. In: Proceedings of the Third Workshop on Computational Models of Narrative, vol. 1981, pp. 91–99 (2012)Google Scholar
  7. 7.
    Fong, T., Nourbakhsh, I., Dautenhahn, K.: A survey of socially interactive robots. Robot. Auton. Syst. 42(3), 143–166 (2003)CrossRefMATHGoogle Scholar
  8. 8.
    Forlizzi, J., Disalvo, C.: Service robots in the domestic environment: a study of the roomba vacuum in the home. In: Design, pp. 258–265 (2006)Google Scholar
  9. 9.
    Fournier-Viger, P., Faghihi, U., Nkambou, R., Nguifo, E.M.: Cmrules: mining sequential rules common to several sequences. Knowl.-Based Syst. 25(1), 63–76 (2012)CrossRefGoogle Scholar
  10. 10.
    Gatt, A., Reiter, E.: Simplenlg: a realisation engine for practical applications. In: Proceedings of the 12th European Workshop on Natural Language Generation, pp. 90–93. Association for Computational Linguistics (2009)Google Scholar
  11. 11.
    Gervás, P., Díaz-Agudo, B., Peinado, F., Hervás, R.: Story plot generation based on cbr. Knowl.-Based Syst. 18(4), 235–242 (2005)CrossRefGoogle Scholar
  12. 12.
    Hayes, A.F., Krippendorff, K.: Answering the call for a standard reliability measure for coding data. Commun. Methods Measures 1(1), 77–89 (2007)CrossRefGoogle Scholar
  13. 13.
    Labov, W., Waletzky, J.: Narrative analysis: oral versions of personal experience (1997)Google Scholar
  14. 14.
    Lareau, F., Dras, M., Dale, R.: Detecting interesting event sequences for sports reporting. In: Proceedings of the 13th European Workshop on Natural Language Generation, pp. 200–205. Association for Computational Linguistics (2011)Google Scholar
  15. 15.
    Mairesse, F., Walker, M.: Personage: personality generation for dialogue. In: Annual Meeting-Association For Computational Linguistics, vol. 45, p. 496 (2007)Google Scholar
  16. 16.
    Pratt, M.L.: Toward a Speech Act Theory of Literary Discourse. Indiana University Press, Bloomington (1977) Google Scholar
  17. 17.
    Searle, J.R.: A taxonomy of illocutionary acts. In: Gunderson, K. (ed.) Language, Mind and Knowledge, pp. 344–369. University of Minnesota Press, Minneapolis (1975)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of California Santa CruzSanta CruzUSA
  2. 2.Institute for Creative TechnologiesUniversity of Southern CaliforniaLos AngelesUSA

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