Modélisation cognitive de la pertinence narrative en vue de l'évaluation et de la génération de récits

Abstract : Humans devote a considerable amount of time to producing narratives. Whatever a story is used for (whether to entertain or to teach), it must be relevant. Relevant stories must be believable and interesting. The field of computational generation of narratives has explored many ways of generating narratives, especially well-formed and understandable ones. The question of what makes a story interesting has however been largely ignored or barely addressed. Only some specific aspects of narrative interest have been considered. No general theoretical framework that would serve as guidance for the generation of interesting and believable narratives has been provided. The aim of this thesis is to introduce a cognitive model of situational interest and use it to offer formal criteria to decide to what extent a story is relevant. Such criteria could guide the development of a cognitively plausible model of story generation.
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Antoine Saillenfest. Modélisation cognitive de la pertinence narrative en vue de l'évaluation et de la génération de récits. Intelligence artificielle [cs.AI]. Télécom ParisTech, 2015. Français. ⟨NNT : 2015ENST0073⟩. ⟨tel-01437849⟩

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