Personalized Interactive Narratives via Sequential Recommendation of Plot Points

Yu, Hong; Riedl, Mark O.

doi:10.1109/tciaig.2013.2282771

Cited by 24 publications

(14 citation statements)

References 17 publications

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“…Research in the domain of interactive storytelling often focuses on games (see e.g. Bulitko, 2013 andYu &Riedl, 2014) or, in connection with location based services, on museum or tourist applications (see e.g. Katifori, 2018 andPujol et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

Personalisation of data-driven storytelling

Meier¹,

Dinklage²,

Glinka

2021

Abstr. Int. Cartogr. Assoc.

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Section: Related Workmentioning

confidence: 99%

Personalisation of data-driven storytelling

Meier¹,

Dinklage²,

Glinka

2021

Abstr. Int. Cartogr. Assoc.

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“…In contrast to traditional narrative media such as books, animation, and film, interactive narrative generation enables the creation of rich, story-centric experiences in which users are active participants who shape the events and outcomes of an unfolding narrative. A range of computational approaches have been investigated for interactive narrative generation, most recently machine learning techniques such as collaborative filtering [18], dynamic Bayesian networks [19], and reinforcement learning [8,20]. These latter techniques comprise a family of data-driven systems that dynamically personalize interactive narratives by training generative models "bottom-up" from corpora of example story data.…”

Section: Ai-driven Adaptive Technologies For Adolescent Preventive Healthcarementioning

confidence: 99%

Artificial Intelligence for Personalized Preventive Adolescent Healthcare

Rowe

Lester

2020

Journal of Adolescent Health

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Recent advances in artificial intelligence (AI) are creating new opportunities for personalizing technology-based health interventions to adolescents. This article provides a computer science perspective on how emerging AI technologiesdintelligent learning environments, interactive narrative generation, user modeling, and adaptive coachingdcan be utilized to model adolescent learning and engagement and deliver personalized support in adaptive health technologies. Many of these technologies have emerged from human-centered applications of AI in education, training, and entertainment. However, their application to improving healthcare, to date, has been comparatively limited. We illustrate the opportunities provided by AI-driven adaptive technologies for adolescent preventive healthcare by describing a vision of how future adolescent preventive health interventions might be delivered both inside and outside of the clinic. Key challenges posed by AI-driven health technologies are also presented, including issues of privacy, ethics, encoded bias, and integration into clinical workflows and adolescent lives. Examples of empirical findings about the effectiveness of AI technologies for user modeling and adaptive coaching are presented, which underscore their promise for application toward adolescent health. The article concludes with a brief discussion of future research directions for the field, which is well positioned to leverage AI to improve adolescent health and well-being.

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“…To bypass the brittleness of rules others have used datadriven techniques that optimize game parameters toward design goals. Hastings et al [9], Shaker et al [21], Liapis et al [13] and Yu and Riedl [26] model player preferences using neuro-evolutionary or machine learning techniques and optimize the output of these models to select potential game parameters. Harrison and Roberts [8] optimize player retention and Zook and Riedl [28] optimize game difficulty with similar techniques.…”

Section: Online Game Adaptationmentioning

confidence: 99%

Automatic Playtesting for Game Parameter Tuning via Active Learning

Zook,

Fruchter,

Riedl

2019

Preprint

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Game designers use human playtesting to gather feedback about game design elements when iteratively improving a game. Playtesting, however, is expensive: human testers must be recruited, playtest results must be aggregated and interpreted, and changes to game designs must be extrapolated from these results. Can automated methods reduce this expense? We show how active learning techniques can formalize and automate a subset of playtesting goals. Specifically, we focus on the low-level parameter tuning required to balance a game once the mechanics have been chosen. Through a case study on a shoot-'em-up game we demonstrate the efficacy of active learning to reduce the amount of playtesting needed to choose the optimal set of game parameters for two classes of (formal) design objectives. This work opens the potential for additional methods to reduce the human burden of performing playtesting for a variety of relevant design concerns.

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Personalized Interactive Narratives via Sequential Recommendation of Plot Points

Cited by 24 publications

References 17 publications

Personalisation of data-driven storytelling

Personalisation of data-driven storytelling

Artificial Intelligence for Personalized Preventive Adolescent Healthcare

Automatic Playtesting for Game Parameter Tuning via Active Learning

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