Computational Approaches to Storytelling and Creativity

Gervás, Pablo

doi:10.1609/aimag.v30i3.2250

Cited by 110 publications

(71 citation statements)

References 21 publications

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“…Most historical overviews of expressive systems in the literature today are confined to short blurbs in related-works sections, and upon further investigation these brief histories are often at best incomplete. For example, most accounts of the history of story generation cite Sheldon Klein's automatic novel writer [2] as the earliest known system (e.g., [1,6]), but in a recent paper undertaking the HEX initiative we have demonstrated that three other forgotten systems preceded Klein's [3].…”

Section: Overview: Topic and Goalsmentioning

confidence: 99%

1st Workshop on the History of Expressive Systems

Ryan

Nelson

2017

Interactive Storytelling

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The first meeting of a new workshop on the History of Expressive Systems (HEX) is being held at ICIDS 2017. By 'expressive systems', we broadly mean computer systems (or predigital procedural methods) that were developed with expressive or creative aims; this is meant to be a superset of the areas called creative AI, expressive AI, videogame AI, computational creativity, interactive storytelling, computational narrative, procedural music, computer poetry, generative art, and more. While much of this purview intersects with projects in artificial intelligence, we are more broadly interested in procedural methods of all kinds (even predigital ones, as mentioned above).HEX is meant to illuminate and celebrate the history of systems in this area, especially the untold histories of projects that are today forgotten or relatively unknown. Most historical overviews of expressive systems in the literature today are confined to short blurbs in related-works sections, and upon further investigation these brief histories are often at best incomplete. For example, most accounts of the history of story generation cite Sheldon Klein's automatic novel writer [2] as the earliest known system (e.g., [1,6]), but in a recent paper undertaking the HEX initiative we have demonstrated that three other forgotten systems preceded Klein's [3].Why should we care about old, forgotten work? If we view expressive systems as a vast design space, we can think of each implemented system as an exploratory vessel that ventures into a previously uncharted sector. If these exploratory missions are successful, they signal directions that future systems may move further into to find greater success. When success is not had, the failed projects tell us which areas to avoid. In this way, we learn about spaces that incrementalist research may push further into, dead sectors that we should not return to, and all the other still uncharted areas that we do not know much about at all. Thus, both good and bad systems generate new knowledge that is useful to contemporary and future practitioners. But when we forget about past systems-novel explorations in design space-we lose the knowledge that was generated by those systems: we forget what has been explored and what has not, and which areas are worth exploring further. In our own historical re-

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Section: Overview: Topic and Goalsmentioning

confidence: 99%

1st Workshop on the History of Expressive Systems

Ryan

Nelson

2017

Interactive Storytelling

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“…The computational creativity community has worked on automatic creation of fi stories for many years [32]. Most of this research has focused on high-level constructs such as plots and character development [6], although some work has also been done on lower-level linguistic issues [33].…”

Section: Narrative In Nlgmentioning

confidence: 99%

Personal storytelling: Using Natural Language Generation for children with complex communication needs, in the wild…

Tintarev

Reiter

Black

et al. 2016

International Journal of Human-Computer Studies

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This paper describes a Natural Language Generation system (NLG), How was School Today? that automatically creates a personal narrative from sensor data and other media (photos and audio). It can be used by children with complex communication needs in schools to support interactive narrative about personal experiences. The robustness of story generation to missing data was identified as a key area for improvement in a feasibility study of the system at a fi special needs school. This paper therefore suggests three possible methods for generating stories from unstructured data: clustering by voice recording, by location, or by time. Clustering based on voice recordings resulted in stories that were perceived as most easy to read, and to make most sense, by parents in a quantitative evaluation. This method was implemented in the live system, which was developed and evaluated iteratively at a second special needs school with children with diff t usage profi Open challenges and possibilities for nlg in augmented and alternative communication are also discussed.

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“…Built on the age-old tradition of storytelling, algorithmically generated stories can be used in a wide variety of domains such as computer games, training and education. In addition, research in story generation may shed light into the broader phenomena of computational creativity [6].…”

Section: Automatic Story Generationmentioning

confidence: 99%

“…However, in some domains, such as automated story generation [6] (which we used as our application domain), defining an evaluation function that captures the quality of a solution is a very hard problem. For this reason, we propose a new case revision approach that integrates argumentation into the case reuse process.…”

Section: Introductionmentioning

confidence: 99%

Argument-Based Case Revision in CBR for Story Generation

Ontañón

Plaza

Zhu

2015

Case-Based Reasoning Research and Development

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Abstract. This paper presents a new approach to case revision in casebased reasoning based on the idea of argumentation. Previous work on case reuse has proposed the use of operations such as case amalgamation (or merging), which generate solutions by combining information coming from different cases. Such approaches are often based on exploring the search space of possible combinations looking for a solution that maximizes a certain criteria. We show how Revise can be performed by arguments attacking specific parts of a case produced by Reuse, and how they can guide and prevent repeating pitfalls in future cases. The proposed approach is evaluated in the task of automatic story generation.

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Computational Approaches to Storytelling and Creativity

Cited by 110 publications

References 21 publications

1st Workshop on the History of Expressive Systems

1st Workshop on the History of Expressive Systems

Personal storytelling: Using Natural Language Generation for children with complex communication needs, in the wild…

Argument-Based Case Revision in CBR for Story Generation

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