Abstract
Conflict is an essential element of interesting stories. In previous work, we proposed a formal model of narrative conflict along with 4 quantitative dimensions which can be used to distinguish one conflict from another based on context: balance, directness, intensity, and resolution. This paper presents the results of an experiment designed to measure how well these metrics predict the responses of human readers when asked to measure these same values in a set of four stories. We conclude that our metrics are able to rank stories similarly to human readers for each of these four dimensions.
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References
Abbott, H.P.: The Cambridge Introduction to Narrative. Cambridge U. Pr. (2008)
Barber, H., Kudenko, D.: Dynamic Generation of Dilemma-Based Interactive Narratives. In: AIIDE (2007)
Bhattacharyya, A.: On a measure of divergence between two statistical populations defined by their probability distributions. Bull. Calcutta Math. Soc. 35(99-109), 4 (1943)
Booth, M.: The ai systems of left 4 dead. In: Keynote, AIIDE (2009)
Crawford, C.: Chris Crawford on Game Design. New Riders (2003)
Egri, L.: The Art of Dramatic Writing. Wildside (1988)
Fikes, R., Nilsson, N.J.: STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving. AI 2(3/4), 189–208 (1971)
Fleiss, J.L., Levin, B., Paik, M.C.: Statistical Methods for Rates and Proportions, 3rd edn. John Wiley & Sons (2003)
Gerrig, R.J.: Experiencing Narrative Worlds: On the Psychological Activities of Reading. Yale U. Pr. (1993)
Hamming, R.W.: Error detecting and error correcting codes. Bell System Technical Journal 29(2), 147–160 (1950)
Herman, D., Jahn, M., Ryan, M.L.: Routledge Encyclopedia of Narrative Theory. Routledge (2005)
Kendall, M.: Rank Correlation Methods. Griffin (1948)
Peinado, F., Gervás, P.: Evaluation of automatic generation of basic stories. New Generation Computing 24(3), 289–302 (2006)
Pérez y Pérez, R., Sharples, M.: MEXICA: A Computer Model of a Cognitive Account of Creative Writing. Journal of Experimental & Theoretical AI 13(2), 119–139 (2001)
Ryan, M.L.: Possible Worlds, Artificial Intelligence, and Narrative Theory. Indiana U. Pr. (1991)
Szilas, N.: IDtension: A Narrative Engine for Interactive Drama. In: TIDSE (2003)
Ware, S.G., Young, R.M.: CPOCL: A Narrative Planner Supporting Conflict. In: AIIDE (2011)
Ware, S.G., Young, R.M.: Toward a Computational Model of Narrative Conflict. Technical Report DGRC-2011-01, DGRC, North Carolina State University, Raleigh, NC, USA (2011), http://dgrc.ncsu.edu/pubs/dgrc-2011-01.pdf
Ware, S.G., Young, R.M.: Validating a Plan-Based Model of Narrative Conflict. In: FDG (2012)
Yannakakis, G.N.: How to model and augment player satisfaction: A review. In: 1st Workshop on Child, Computer, and Interaction (2008)
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Ware, S.G., Young, R.M., Harrison, B., Roberts, D.L. (2012). Four Quantitative Metrics Describing Narrative Conflict. In: Oyarzun, D., Peinado, F., Young, R.M., Elizalde, A., Méndez, G. (eds) Interactive Storytelling. ICIDS 2012. Lecture Notes in Computer Science, vol 7648. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34851-8_2
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DOI: https://doi.org/10.1007/978-3-642-34851-8_2
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