Causal graphs are a useful notation to represent the interplay between the actors as well as the polarity and strength of the relationship that they share. They are used extensively in educational, professional, and industrial contexts to simulate different scenarios, validate behavioral aspects, visualize the connections between different processes, and explore the adversarial effects of changing certain nodes. However, as the size of the causal graphs increase, interpreting them also becomes increasingly tougher. In such cases, new analytical tools are required to enhance the user's comprehension of the graph, both in terms of correctness and speed. To this purpose, this thesis introduces 1) a system that allows for causal exploration of directed graphs, while enabling the user to see the effect of interventions on the target nodes, 2) the use of natural language generation techniques to create a coherent passage explaining the propagation effects, and 3) results of an expert user study validating the efficacy of the narratives in enhancing the user's understanding of the causal graphs. In overall, the system aims to enhance user experience and promote further causal exploration. / Master of Science / Narrative generation is the art of creating coherent snippets of text that cumulatively describe a succession of events, played across a period of time. These goals of narrative generation are also shared by causal graphs – models that encapsulate inferences between the nodes through the strength and polarity of the connecting edges. Causal graphs are an useful mechanism to visualize changes propagating amongst nodes in the system. However, as the graph starts addressing real-world actors and their interactions, it becomes increasingly difficult to understand causal inferences between distant nodes, especially if the graph is cyclic. Moreover, if the value of more than a single node is altered and the cumulative effect of the change is to be perceived on a set of target nodes, it becomes extremely difficult to the human eye. This thesis attempts to alleviate this problem by generating dynamic narratives detailing the effect of one or more interventions on one or more target nodes, incorporating time-series analysis, Wikification, and spike detection. Moreover, the narrative enhances the user's understanding of the change propagation occurring in the system. The efficacy of the narrative was further corroborated by the results of user studies, which concluded that the presence of the narrative aids the user's confidence level, correctness, and speed while exploring the causal network.

A large part of any game development process consists of content creation, which costs both time and effort. Procedural generation techniques exist to help narrative generation, but they are scattered and require extensive manual labour to set up. On top of that, Serious Games content created with these techniques tend to be uninteresting and lack variety which can ultimately lead to the Serious Games missing their intended purpose. This paper delivers a prototype for a modular tool that aims to solve these problems by combining existing narrative generation techniques with common sense database knowledge and player adaptivity techniques. The prototype tool implements Ceptre as a core module for the generation of stories and ConceptNet as a commonsense knowledge database. Two studies have been conducted with content created by the tool. One study tested if generation rules created by commonsense can be used to flesh out stories, while the other one evaluated if adapted stories yield better scores. The results of the first test state that adding rules retrieved through common sense knowledge did not improve story quality, but they can however be used to extend stories without compromising story quality. It also shows that ideally, an extensive natural language processing module should be used to present the stories rather than a basic implementation. The statistically insignificant result of the second test was potentially caused by the compromises taken when conducting the test. Reconduction of this test using real game data, rather than data from the compromised personality test, might be preferable.

Le récit est un outil de communication qui permet aux individus de donner un sens au monde qui les entoure. Il représente une plate-forme pour comprendre et partager leur culture, connaissances et identité. Le récit porte une série d'événements réels ou imaginaires, en provoquant un ressenti, une réaction ou même, déclenche une action. Pour cette raison, il est devenu un sujet d'intérêt pour différents domaines au-delà de la Littérature (Éducation, Marketing, Psychologie, etc.) qui cherchent d'atteindre un but particulier au travers de lui (Persuader, Réfléchir, Apprendre, etc.).Cependant, le récit reste encore sous-développé dans le contexte informatique. Il existent des travaux qui visent son analyse et production automatique. Les algorithmes et implémentations, par contre, restent contraintes à imiter le processus créatif derrière des textes littéraires provenant de sources textuelles. Ainsi, il n'existent pas des approches qui produisent automatiquement des récits dont 1) la source est constitué de matériel non formatées et passé dans la réalité et 2) et le contenu projette une perspective qui cherche à transmettre un message en particulier. Travailler avec des données brutes devient relevante vu qu'elles augmentent exponentiellement chaque jour grâce à l'utilisation d'appareils connectés.Ainsi, vu le contexte du Big Data, nous présentons une approche de génération automatique de récits à partir de données ambiantes. L'objectif est de faire émerger l'expérience vécue d'une personne à partir des données produites pendant une activité humaine. Tous les domaines qui travaillent avec des données brutes pourraient bénéficier de ce travail, tels que l'Éducation ou la Santé. Il s'agit d'un effort interdisciplinaire qui inclut le Traitement Automatique de Langues, la Narratologie, les Sciences Cognitives et l'Interaction Homme-Machine.Cette approche est basée sur des corpus et modèles et comprend la formalisation de ce que nous appelons le récit d'activité ainsi qu'une démarche de génération adaptée. Elle a est composé de 4 étapes : la formalisation des récits d'activité, la constitution de corpus, la construction de modèles d'activité et du récit, et la génération de texte. Chacune a été conçue pour surmonter des contraintes liées aux questions scientifiques posées vue la nature de l'objectif : la manipulation de données incertaines et incomplètes, l'abstraction valide d'après l'activité, la construction de modèles avec lesquels il soit possible la transposition de la réalité gardée dans les données vers une perspective subjective et la rendue en langage naturel. Nous avons utilisé comme cas d'usage le récit d'activité, vu que les pratiquant se servent des appareils connectés, ainsi qu'ils ont besoin de partager son expérience. Les résultats obtenus sont encourageants et donnent des pistes qui ouvrent beaucoup de perspectives de recherche. / Stories are a communication tool that allow people to make sense of the world around them. It represents a platform to understand and share their culture, knowledge and identity. Stories carry a series of real or imaginary events, causing a feeling, a reaction or even trigger an action. For this reason, it has become a subject of interest for different fields beyond Literature (Education, Marketing, Psychology, etc.) that seek to achieve a particular goal through it (Persuade, Reflect, Learn, etc.).However, stories remain underdeveloped in Computer Science. There are works that focus on its analysis and automatic production. However, those algorithms and implementations remain constrained to imitate the creative process behind literary texts from textual sources. Thus, there are no approaches that produce automatically stories whose 1) the source consists of raw material that passed in real life and 2) and the content projects a perspective that seeks to convey a particular message. Working with raw data becomes relevant today as it increase exponentially each day through the use of connected devices.Given the context of Big Data, we present an approach to automatically generate stories from ambient data. The objective of this work is to bring out the lived experience of a person from the data produced during a human activity. Any areas that use such raw data could benefit from this work, for example, Education or Health. It is an interdisciplinary effort that includes Automatic Language Processing, Narratology, Cognitive Science and Human-Computer Interaction.This approach is based on corpora and models and includes the formalization of what we call the activity récit as well as an adapted generation approach. It consists of 4 stages: the formalization of the activity récit, corpus constitution, construction of models of activity and the récit, and the generation of text. Each one has been designed to overcome constraints related to the scientific questions asked in view of the nature of the objective: manipulation of uncertain and incomplete data, valid abstraction according to the activity, construction of models from which it is possible the Transposition of the reality collected though the data to a subjective perspective and rendered in natural language. We used the activity narrative as a case study, as practitioners use connected devices, so they need to share their experience. The results obtained are encouraging and give leads that open up many prospects for research.

This paper will detail the prototyping and subsequent production of an digital narrative experience utilizing the theories of Vladimir Propp. The prototype will examine the theories detailed in Propps Morphology of the Folktale. It will implement Propps narrative functions according to a general scheme, connected by connectives. The prototype will dynamically generate narratives according to this scheme. Finally, this paper will draw conclusions about the advantages of a Propp-based system of narrative generation and the narratives produced compared to other digital narratives, such as hypertext.

A common way to examine children's language production is to analyze their narrative ability, which has been observed in several studies in various languages. The present study aims to analyze Swedish children's stories at microstructural level (grammatical and utterance level) and macrostructural level (discourse level) and examine how these results differ between two narrative elicitation methods. Furthermore, the study aims to investigate if the results from Swedish and English speaking children differ between these two elicitation methods. The present study involved 40 typically developing children aged 7;0–8;11 years with Swedish as their native language from southeastern Sweden. The two narrative elicitation methods used were personal narrative generation and fictional narrative retell. During personal narrative generation the participants were prompted to share their own experiences based on given themes with image support. In the fictional narrative retell participants were asked to retell the tale "Frog, Where are you?" (Mayer, 1969). The participants' two stories were transcribed in the program Systematic Analysis of Language Transcripts (SALT) and analyzed at micro- and macrostructural level. At the microstructural level the following measures were investigated: Total utterances (TU), Mean Length of Utterance in Words (MLU-w), Type Token Ratio (TTR), Percentage of Maze Words (PcMw), Overgeneralization Errors (EO) and Total Number of Words (TNW). For macrostructural analysis the Narrative Scoring Scheme (NSS) was used. At the microstructural level participants achieved higher MLU-w, TTR and PcMw in the fictional narrative retell than in personal narrative generation. TNW and MLU-w correlated with participants' age at fictional narrative retell. The participants generated higher values regarding TU and TNW in personal narrative generation. Analysis of the macrostructure showed that the majority of the participants achieved higher scores in fictional narrative retell than in personal narrative generation. The results regarding the macrostructural level of the fictional narrative retell correlated with the age of the participants. Similar trends between the elicitation methods appeared in the English-speaking participants.