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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.
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.
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.
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
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.
This volume contains scientific papers and case studies presented at Interactive Sto- telling ’08: The First Joint International Conference on Interactive Digital Storytelling (ICIDS), held November 26–29, 2008, in Erfurt, Germany. Interactive Digital Storytelling (IDS) is a cross-disciplinary topic, which explores new uses of interactive technologies for creating and experiencing narratives. IDS is also a huge step forward in games and learning. This can be seen through its ability to enrich virtual characters with intelligent behavior, to allow collaboration of humans and machines in the creative process, and to combine narrative knowledge and user activity in interactive artifacts. IDS involves concepts from many aspects of Computer Science, above all from Artificial Intelligence, with topics such as narrative intelligence, automatic dialogue and drama management, and smart graphics. In order to process stories in real time, traditional storytelling needs to be formalized into computable models by drawing from narratological studies. As it is currently hardly accessible for creators and e- users, there is a need for new authoring concepts and tools supporting the creation of such dynamic stories, allowing for rich and meaningful interaction with the content.
Keywords: analepsis, flashback, surprise arousal, narrative generation.
The core message of this book is: computer games best realise affective interaction. This book brings together contributions from specialists in affective computing, game studies, game artificial intelligence, user experience research, sensor technology, multi-modal interfaces and psychology that will advance the state-of-the-art in player experience research; affect modelling, induction, and sensing; affect-driven game adaptation and game-based learning and assessment. In 3 parts the books covers Theory, Emotion Modelling and Affect-Driven Adaptation, and Applications. This book will be of interest to researchers and scholars in the fields of game research, affective computing, human computer interaction, and artificial intelligence.
The rich programme of ICIDS 2009, comprising invited talks, technical pres- tations and posters, demonstrations, and co-located post-conference workshops clearly underscores the event’s status as premier international meeting in the domain. It thereby con?rms the decision taken by the Constituting Committee of the conference series to take the step forward: out of the national cocoons of its precursors, ICVS and TIDSE, and towards an itinerant platform re?ecting its global constituency. This move re?ects the desire and the will to take on the challenge to stay on the lookout, critically re?ect upon and integrate views and ideas,?ndingsandexperiences,andtopromoteinterdisciplinaryexchange,while ensuring overall coherence and maintaining a sense of direction. This is a signi?cant enterprise: The challenges sought are multifarious and must be addressed consistently at all levels. The desire to involve all research communitiesandstakeholdersmustbematchedbyacknowledgingthedi?erences in established practises and by providing suitable means of guidance and int- duction, exposition and direct interaction at the event itself and of lasting (and increasingly:living) documentation, of which the present proceedings are but an important part.
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.