A Cognitive Approach to Narrative Planning with Believable Characters

In this work, we address the question of generating understandable narratives using a cognitive approach. The requirements of cognitive plausibility are presented. Then an abduction-based cognitive model of the human deliberative reasoning ability is presented. We believe that implementing such a procedure in a narrative context to generate plans would increase the chances that the characters will be perceived as believable. Our suggestion is that the use of a deliberative reasoning procedure can be used as a basis of several strategies to generate interesting stories

Using Unexpected Simplicity to Control Moral Judgments and Interest in Narratives

The challenge of narrative automatic generation is to produce not only coherent, but interesting stories. This study considers the problem within the Simplicity Theory framework. According to this theory, interesting situations must be unexpectedly simple, either because they should have required complex circumstances to be produced, or because they are abnormally simple, as in coincidences. Here we consider the special case of narratives in which characters perform actions with emotional consequences. We show, using the simplicity framework, how notions such as intentions, believability, responsibility and moral judgments are linked to narrative interest

Cognitive modeling of narrative relevance : towards the evaluation and the generation of stories

Une part importante de l’activité de communication humaine est dédiée au récit d’événements (fictifs ou non). Ces récits doivent être cohérents et intéressants pour être pertinents. Dans le domaine de la génération automatique de récits, la question de l’intérêt a souvent été négligée, ou traitée via l’utilisation de méthodes ad hoc, au profit de la cohérence des structures narratives produites. Nous proposons d’aborder le processus de création des récits sous l’angle de la modélisation quantitative de critères de pertinence narrative via l’application d’un modèle cognitif de l’intérêt événementiel. Nous montrerons que cet effort de modélisation peut servir de guide pour concevoir un modèle cognitivement plausible de génération de narrations.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

Can believable characters act unexpectedly?

International audienceUnexpectedness is a major factor controlling interest in narratives. Emotions, for instance, are felt intensely if they are associated with unexpected events. The problem with generating unexpected situations is that either characters, or the whole story, are at risk of being no longer believable. This issue is one of the main problems that make story design a hard task. Writers face it on a case by case basis. The automatic generation of interesting stories requires formal criteria to decide to what extent a given situation is unexpected and to what extent actions are kept believable. This paper proposes such formal criteria and makes suggestions concerning their use in story generation systems

Some Probability Judgments may Rely on Complexity Assessments

Human beings do assess probabilities. Their judgments are however sometimes at odds with probability theory. One possibility is that human cognition is imperfect or flawed in the probability domain, showing biases and errors. Another possibility, that we explore here, is that human probability judgments do not rely on a weak version of probability calculus, but rather on complexity computations. This hypothesis is worth exploring, not only because it predicts some of the probability ‘biases’, but also because it explains human judgments of uncertainty in cases where probability calculus cannot be applied. We designed such a case in which the use of complexity when judging uncertainty is almost transparen

Role of Kolmogorov Complexity on Interest in Moral Dilemma Stories

6 pagesInternational audienceSeveral studies have highlighted the combined role of emotions and reasoning in the determination of judgments about morality. Here we explore the influence of Kolmogorov complexity in the determination, not only of moral judgment, but also of the associated narrative interest. We designed an experiment to test the predictions of our complexity-based model when applied to moral dilemmas. It confirms that judgments about interest and morality may be explained in part by discrepancies in complexity. This preliminary study suggests that cognitive computations are involved in decision-making about emotional outcomes

A computational model of moral and legal responsibility via simplicity theory

International audience<p>Responsibility, as referred to in everyday life, as explored in moral philosophyand debated in jurisprudence, is a multiform, ill-defined but inescapablenotion for reasoning about actions. Its presence in all social constructs suggests theexistence of an underlying cognitive base. Following this hypothesis, and buildingupon simplicity theory, the paper proposes a novel computational approach.</p

Role of simplicity in creative behaviour: The case of the poietic generator

International audienc