A Computational Model Of Narrative Generation For Suspense
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Author | : Yun Gyung Cheong |
Publisher | : |
Total Pages | : 150 |
Release | : 2007 |
Genre | : |
ISBN | : 9780549076339 |
The generation of stories by computers, with applications ranging from computer games to education and training, has been the focus of research by computational linguists and AI researchers since the early 1970s. Although several approaches have shown promise in their ability to generate narrative, there has been little research on the generation of stories that evoke specific cognitive and affective responses in their readers. The goal of this research is to develop a system that produces a narrative designed specifically to evoke a targeted degree of suspense, a significant contributor to the level of engagement experienced by users of interactive narrative systems. The system that I present takes as input a plan data structure representing the goals of a storyworld's characters and the actions they perform in pursuit of them. Adapting theories developed by cognitive psychologists, my system uses a plan-based model of narrative comprehension to determine the final content of the story in order to manipulate a reader's level of suspense in specific ways. In this thesis, I outline the various components of the system and describe an empirical evaluation that I used to determine the efficacy of my techniques. The evaluation provides strong support for the claim that the system is effective in generating suspenseful stories.
Author | : |
Publisher | : |
Total Pages | : |
Release | : 2004 |
Genre | : |
ISBN | : |
The generation of stories by computers, with applications ranging from computer games to education and training, has been the focus of research by computational linguists and AI researchers since the early 1970s. Although several approaches have shown promise in their ability to generate narrative, there has been little research on the generation of stories that evoke specific cognitive and affective responses in their readers. The goal of this research is to develop a system that produces a narrative designed specifically to evoke a targeted degree of suspense, a significant contributor to the level of engagement experienced by users of interactive narrative systems. The system that I present takes as input a plan data structure representing the goals of a storyworld's characters and the actions they perform in pursuit of them. Adapting theories developed by cognitive psychologists, my system uses a plan-based model of narrative comprehension to determine the final content of the story in order to manipulate a reader's level of suspense in specific ways. In this thesis, I outline the various components of the system and describe an empirical evaluation that I used to determine the efficacy of my techniques. The evaluation provides strong support for the claim that the system is effective in generating suspenseful stories.
Author | : Brian O'Neill |
Publisher | : |
Total Pages | : |
Release | : 2013 |
Genre | : Artificial intelligence |
ISBN | : |
In this dissertation, I present Dramatis, a computational human behavior model of suspense based on Gerrig and Bernardo's de nition of suspense. In this model, readers traverse a search space on behalf of the protagonist, searching for an escape from some oncoming negative outcome. As the quality or quantity of escapes available to the protagonist decreases, the level of suspense felt by the audience increases. The major components of Dramatis are a model of reader salience, used to determine what elements of the story are foregrounded in the reader's mind, and an algorithm for determining the escape plan that a reader would perceive to be the most likely to succeed for the protagonist. I evaluate my model by comparing its ratings of suspense to the self-reported suspense ratings of human readers. Additionally, I demonstrate that the components of the suspense model are sufficient to produce these human-comparable ratings.
Author | : Inderjeet Mani |
Publisher | : Springer Nature |
Total Pages | : 124 |
Release | : 2022-05-31 |
Genre | : Computers |
ISBN | : 3031021479 |
The field of narrative (or story) understanding and generation is one of the oldest in natural language processing (NLP) and artificial intelligence (AI), which is hardly surprising, since storytelling is such a fundamental and familiar intellectual and social activity. In recent years, the demands of interactive entertainment and interest in the creation of engaging narratives with life-like characters have provided a fresh impetus to this field. This book provides an overview of the principal problems, approaches, and challenges faced today in modeling the narrative structure of stories. The book introduces classical narratological concepts from literary theory and their mapping to computational approaches. It demonstrates how research in AI and NLP has modeled character goals, causality, and time using formalisms from planning, case-based reasoning, and temporal reasoning, and discusses fundamental limitations in such approaches. It proposes new representations for embedded narratives and fictional entities, for assessing the pace of a narrative, and offers an empirical theory of audience response. These notions are incorporated into an annotation scheme called NarrativeML. The book identifies key issues that need to be addressed, including annotation methods for long literary narratives, the representation of modality and habituality, and characterizing the goals of narrators. It also suggests a future characterized by advanced text mining of narrative structure from large-scale corpora and the development of a variety of useful authoring aids. This is the first book to provide a systematic foundation that integrates together narratology, AI, and computational linguistics. It can serve as a narratology primer for computer scientists and an elucidation of computational narratology for literary theorists. It is written in a highly accessible manner and is intended for use by a broad scientific audience that includes linguists (computational and formal semanticists), AI researchers, cognitive scientists, computer scientists, game developers, and narrative theorists. Table of Contents: List of Figures / List of Tables / Narratological Background / Characters as Intentional Agents / Time / Plot / Summary and Future Directions
Author | : Inderjeet Mani |
Publisher | : Morgan & Claypool Publishers |
Total Pages | : 145 |
Release | : 2013 |
Genre | : Computers |
ISBN | : 1608459810 |
The field of narrative (or story) understanding and generation is one of the oldest in natural language processing (NLP) and artificial intelligence (AI), which is hardly surprising, since storytelling is such a fundamental and familiar intellectual and social activity. In recent years, the demands of interactive entertainment and interest in the creation of engaging narratives with life-like characters have provided a fresh impetus to this field. This book provides an overview of the principal problems, approaches, and challenges faced today in modeling the narrative structure of stories. The book introduces classical narratological concepts from literary theory and their mapping to computational approaches. It demonstrates how research in AI and NLP has modeled character goals, causality, and time using formalisms from planning, case-based reasoning, and temporal reasoning, and discusses fundamental limitations in such approaches. It proposes new representations for embedded narratives and fictional entities, for assessing the pace of a narrative, and offers an empirical theory of audience response. These notions are incorporated into an annotation scheme called NarrativeML. The book identifies key issues that need to be addressed, including annotation methods for long literary narratives, the representation of modality and habituality, and characterizing the goals of narrators. It also suggests a future characterized by advanced text mining of narrative structure from large-scale corpora and the development of a variety of useful authoring aids. This is the first book to provide a systematic foundation that integrates together narratology, AI, and computational linguistics. It can serve as a narratology primer for computer scientists and an elucidation of computational narratology for literary theorists. It is written in a highly accessible manner and is intended for use by a broad scientific audience that includes linguists (computational and formal semanticists), AI researchers, cognitive scientists, computer scientists, game developers, and narrative theorists.
Author | : |
Publisher | : |
Total Pages | : |
Release | : 2004 |
Genre | : |
ISBN | : |
This dissertation describes work to develop a planning-based computational model of narrative generation designed to elicit surprise in the mind of a reader. To this end, my approach makes use of two narrative devices ââ'¬â€œ flashback and foreshadowing. While surprise plays an important role for attention focusing, learning, and creativity, little effort has been made to build a computational framework for surprise arousal in narrative. In my computational model, flashback provides a backstory to explain what causes a surprising outcome, while foreshadowing gives hints about the surprise before it occurs. In this work I focus on the arousal of surprise emotion as a cognitive response which is based on a reader's cognitive appraisal of a given situation. In this dissertation I present Prevoyant, a planning-based computational model of surprise arousal in narrative generation, and analyze the effectiveness of Prevoyant. To build a computational model of the unexpectedness in surprise, I adopt a cognitive model of surprise based on expectation failure. There are two contributions made by this dissertation. First, I present a computational framework for narrative generation designed to elicit surprise. The approach makes use of a two-tier model of narrative and draws on Structural Affect Theory, which claims that a readerââ'¬â"¢s emotions such as surprise or suspense are closely related to narrative structure. Second, I present a methodology to evaluate surprise in narrative generation using a planning-based approach based on the cognitive model of surprise causes. The results of the experiments that I conducted show strong support that my system effectively generates a discourse structure for surprise arousal in narrative.
Author | : |
Publisher | : |
Total Pages | : |
Release | : 2003 |
Genre | : |
ISBN | : |
In the cognitive psychology literature, it has been found that during suspenseful parts of a narration, a person uses a mental model similar to that of problem solving. The person is feeling suspenseful because of the uncertainty that the protagonist of the narration can find a solution to the problem (typically a threat of misfortune). This narrative comprehension employed during the problem solving is very similar to the way in which an AI partial order least commitment hierarchical planner attempts to find a solution plan. So, we have used such a planner, the Longbow planner, to represent the mental model formed by the audience, and came up with a computational model of suspense that can predict the suspense level at any point of a narration (generated by the Longbow planner for that story world).
Author | : Ulrike Spierling |
Publisher | : Springer Science & Business Media |
Total Pages | : 345 |
Release | : 2008-11-13 |
Genre | : Computers |
ISBN | : 3540894241 |
This book constitutes the refereed proceedings of the First Joint International Conference on Interactive Digital Storytelling, ICIDS 2008, held in Erfurt, Germany, in November 2008. The 19 revised full papers, 5 revised short papers, and 5 poster papers presented together with 3 invited lectures and 8 demo papers were carefully reviewed and selected from 62 submission. The papers are organized in topical sections on future perspectives on interactive digital storytelling, interactive storytelling applications, virtual characters and agents, user experience and dramatic immersion, architectures for story generation, models for drama management and interacting with stories, as well as authoring and creation of interactive narrative.
Author | : Byung Chull Bae |
Publisher | : |
Total Pages | : 148 |
Release | : 2009 |
Genre | : |
ISBN | : |
Keywords: analepsis, flashback, surprise arousal, narrative generation.
Author | : Sidney D ́Mello |
Publisher | : Springer |
Total Pages | : 651 |
Release | : 2011-10-18 |
Genre | : Computers |
ISBN | : 3642246001 |
The two-volume set LNCS 6974 and LNCS 6975 constitutes the refereed proceedings of the Fourth International Conference on Affective Computing and Intelligent Interaction, ACII 2011, held in Memphis,TN, USA, in October 2011. The 135 papers in this two volume set presented together with 3 invited talks were carefully reviewed and selected from 196 submissions. The papers are organized in topical sections on recognition and synthesis of human affect, affect-sensitive applications, methodological issues in affective computing, affective and social robotics, affective and behavioral interfaces, relevant insights from psychology, affective databases, Evaluation and annotation tools.