Abstract
Current approaches to story generation do not utilize models of human affect to create stories with dramatic arc, suspense, and surprise. This paper describes current and future work towards computational models of affective responses to stories for the purpose of augmenting computational story generators. I propose two cognitively plausible models of suspense and surprise responses to stories. I also propose methods for evaluating these models by comparing them to actual human responses to stories. Finally, I propose the implementation of these models as a heuristic in a search-based story generation system. By using these models as a heuristic, the story generation system will favor stories that are more likely to produce affective responses from human readers.
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References
Bruner, J.: The Narrative Construction of Reality. Critical Inquiry 18, 1–21 (1991)
Gerrig, R.J.: Experiencing Narrative Worlds: On the Psychological Activities of Reading. Yale University Press, New Haven (1993)
Aristotle: The Poetics (T. Buckley trans.). Prometheus Books, Buffalo (1992) (Original work published 350 B.C.E.)
Zagalo, N., Barker, A., Branco, V.: Story reaction structures to emotion detection. In: Proceedings of the 1st ACM Workshop of Story Representation, Mechanism, and Context, pp. 33–38. ACM Press, New York (2004)
Branigan, E.: Narrative Comprehension and Film. Routledge, New York (1992)
Abbott, H.P.: The Cambridge Introduction to Narrative. Cambridge University Press, Cambridge (2008)
Gerrig, R.J., Bernardo, A.B.I.: Readers as Problem-Solvers in the Experience of Suspense. Poetics 22, 459–472 (1994)
Cheong, Y.-G.: A Computational Model of Narrative Generation for Suspense. Doctoral dissertation, North Carolina State University (2007)
Bae, B.-C., Young, R.M.: A Use of Flashback and Foreshadowing for Surprise Arousal in Narrative Using a Plan-Based Approach. In: Spierling, U., Szilas, N. (eds.) ICIDS 2008. LNCS, vol. 5334, pp. 156–167. Springer, Heidelberg (2008)
Pérez y Pérez, R., Sharples, M.: MEXICA: A Computational Model of a Cognitive Account of Creative Writing. Journal of Experimental and Theoretical Artificial Intelligence 13, 119–139 (2001)
Riedl, M.O., Young, R.M.: Narrative Planning: Balancing Plot and Character. Journal of Artificial Intelligence Research 39, 217–268 (2010)
Porteous, J., Teutenberg, J., Pizzi, D., Cavazza, M.: Visual Programming of Plan Dynamics using Constraints and Landmarks. In: Proceedings of the 21st International Conference on Automated Planning and Scheduling, pp. 186–193. AAAI Press, Freiburg (2011)
Mateas, M.: Interactive Drama, Art and Artificial Intelligence. Doctoral dissertation, Carnegie Mellon University (2002)
O’Neill, B., Riedl, M.: Simulating the Everyday Creativity of Readers. In: Proceedings of the Second International Conference on Computational Creativity, pp. 153–158. AAAI Press, Mexico City (2011)
O’Neill, B., Riedl, M.: Toward a Computational Framework of Suspense and Dramatic Arc. In: D´Mello, S., et al. (eds.) ACII 2011, Part I. LNCS, vol. 6974, pp. 246–255. Springer, Heidelberg (2011)
Graesser, A.C., Singer, M., Trabasso, T.: Constructing Inferences During Narrative Text Comprehension. Psychological Review 101, 371–395 (1994)
MacLeod, C., Campbell, L.: Memory Accessibility and Probability Judgments: An Experimental Evaluation of the Availability Heuristic. Journal of Personality and Social Psychology 63, 890–902 (1992)
Orkin, J.D.: Learning Plan Networks in Conversational Video Games. Masters of Science thesis, Massachusetts Institute of Technology (2007)
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O’Neill, B. (2011). Toward a Computational Model of Affective Responses to Stories for Augmenting Narrative Generation. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24571-8_28
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DOI: https://doi.org/10.1007/978-3-642-24571-8_28
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