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Although both deal with narratives, the two disciplines of Narrative Theory (NT) and Computational Story Composition (CSC) rarely exchange insights and ideas or engage in collaborative research. The former has its roots in the humanities, and attempts to analyze literary texts to derive an understanding of the concept of narrative. The latter is in the domain of Artificial Intelligence, and investigates the autonomous composition of fictional narratives in a way that could be deemed creative. The two disciplines employ different research methodologies at contradistinct levels of abstraction, making simultaneous research difficult, while a close exchange between the two disciplines would undoubtedly be desirable, not least because of the complementary approach to their object of study. This book, From Narratology to Computational Story Composition and Back, describes an exploratory study in generative modeling, a research methodology proposed to address the methodological differences between the two disciplines and allow for simultaneous NT and CSC research. It demonstrates how implementing narratological theories as computational, generative models can lead to insights for NT, and how grounding computational representations of narrative in NT can help CSC systems to take over creative responsibilities. It is the interplay of these two strands that underscores the feasibility and utility of generative modeling. The book is divided into 6 chapters: an introduction, followed by chapters on plot, fictional characters, plot quality estimation, and computational creativity, wrapped up by a conclusion. The book will be of interest to all those working in the fields of narrative theory and computational creativity.
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 book is concerned with narrative in digital media that changes according to user input—Interactive Digital Narrative (IDN). It provides a broad overview of current issues and future directions in this multi-disciplinary field that includes humanities-based and computational perspectives. It assembles the voices of leading researchers and practitioners like Janet Murray, Marie-Laure Ryan, Scott Rettberg and Martin Rieser. In three sections, it covers history, theoretical perspectives and varieties of practice including narrative game design, with a special focus on changes in the power relationship between audience and author enabled by interactivity. After discussing the historical development of diverse forms, the book presents theoretical standpoints including a semiotic perspective, a proposal for a specific theoretical framework and an inquiry into the role of artificial intelligence. Finally, it analyses varieties of current practice from digital poetry to location-based applications, artistic experiments and expanded remakes of older narrative game titles.
Authoring, its tools, processes, and design challenges are key issues for the Interactive Digital Narrative (IDN) research community. The complexity of IDN authoring, often involving stories co-created by procedures and user interaction, creates confusion for tool developers and raises barriers for new authors. This book examines these issues from both the tool designer and the author’s perspective, discusses the poetics of IDN and how that can be used to design authoring tools, explores diverse forms of IDN and their demands, and investigates the challenges around conducting research on IDN authoring. To address these challenges, the chapter authors incorporate a range of interdisciplinary perspectives on ‘The Authoring Problem’ in IDN. While existing texts provide ‘how-to’ guidance for authors, this book is a primer for research and practice-based investigations into the authoring problem, collecting the latest thoughts about this area from key researchers and practitioners.