Automated scenario generation

Zook, Alexander; Lee-Urban, Stephen; Riedl, Mark O.; Holden, Heather K.; Sottilare, Robert A.; Brawner, Keith

doi:10.1145/2282338.2282371

Cited by 54 publications

(10 citation statements)

References 7 publications

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“…As defined in Zook et al (2012), "scenario diversity is a measurement of variations among the content of scenarios." The given scenario of the training data may reflect the temporal variability of daily precipitation, evapotranspiration, and other conditions.…”

Section: Scenario Diversity Of Training (Or Prior) Datamentioning

confidence: 99%

A robust data‐worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning

Wang

Shi

Lin

et al. 2020

Vadose Zone Journal

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As the collection of soil moisture data is often costly, it is essential to implement dataworth analysis in advance to obtain a cost-effective data collection scheme. In previous data-worth analysis, the model structural error is often neglected. In this paper, we propose a robust data-worth analysis framework based on a hybrid data assimilation method. By constructing Gaussian process (GP) error model, this study attempts to alleviate biased data-worth assessments caused by unknown model structural errors, and to excavate complementary values of multisource data without resorting to multiple governing equations. The results demonstrated that this proposed framework effectively identified and compensated for complex model structural errors. By training prior data, more accurate potential observations were obtained and data-worth estimation accuracy was improved. The scenario diversity played a crucial role in establishing an effective GP training system. The integration of soil temperature into GP training unraveled new information and improved the data-worth estimation. Instead of traditional evapotranspiration calculations, the direct inclusion of easy-toobtain meteorological data into GP training yielded better data-worth assessment.

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Section: Scenario Diversity Of Training (Or Prior) Datamentioning

confidence: 99%

A robust data‐worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning

Wang

Shi

Lin

et al. 2020

Vadose Zone Journal

View full text Add to dashboard Cite

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“…During each event, qualitative data was collected thanks to pre-event and post-event surveys conducted on the students. The former was based on the questionnaire models implemented in other game jam events [50], while the post-event survey was specifically designed for this study. A total amount of 90 surveys were conducted, 30 per event.…”

Section: Methodsmentioning

confidence: 99%

Game Jams as Valuable Tools for the Development of 21st-Century Skills

Espinosa

2022

Sustainability

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The concept of 21st-century skills refers to the knowledge, skills, and emotions that are critical to successfully navigating today’s world. Game jams can act as spaces to develop these skills and thus boost cooperative learning, problem-based learning, or co-creation. Additionally, game jams offer opportunities to improve collaboration and creativity skills, among others. This paper summarizes three years of activities designing and studying game jams to develop 21st-century skills, focused on Mexican students aged 12–16 years old. Data were compiled through direct observation, open-ended questionnaires, and interviews and were subject to thematic analysis in order to construct new knowledge on a previously underexplored topic. The results suggest that game jams are valuable tools to develop 21st-century skills, and, although the outcomes of skill evaluation may vary and may be difficult to verify, the participants reported increased skills, such as creativity or collaboration. Finally, this paper provides recommendations based on the research and practice conducted by the authors on how to use game jams to develop 21st-century skills and different ways to organize game jams, along with the resources needed.

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“…In this context, creativity is defined as "the ability to generate novel and valuable ideas" [7]. Prominent applications of creative computational models and narrative intelligence [40] in the form of automatic story generation include entertainment [7,54,83], gaming [2,28,75], education and training [8,28,29], and reconnaissance [88]. Alhussain et al have surveyed the textual story generation space, spanning resources, corpora, evaluation methods, and factors of story "interestingness," which feed into applications [1].…”

Section: Computational Image-to-text Models and Visual Storytellingmentioning

confidence: 99%

Envisioning Narrative Intelligence: A Creative Visual Storytelling Anthology

Halperin

Lukin

2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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In this paper, we collect an anthology of 100 visual stories from authors who participated in our systematic creative process of improvised story-building based on image sequences. Following close reading and thematic analysis of our anthology, we present five themes that characterize the variations found in this creative visual storytelling process: (1) Narrating What is in Vision vs. Envisioning; (2) Dynamically Characterizing Entities/Objects; (3) Sensing Experiential Information About the Scenery; (4) Modulating the Mood; (5) Encoding Narrative Biases. In understanding the varied ways that people derive stories from images, we offer considerations for collecting story-driven training data to inform automatic story generation. In correspondence with each theme, we envision narrative intelligence criteria for computational visual storytelling as: creative, reliable, expressive, grounded, and responsible. From these criteria, we discuss how to foreground creative expression, account for biases, and operate in the bounds of visual storyworlds.

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Automated scenario generation

Cited by 54 publications

References 7 publications

A robust data‐worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning

A robust data‐worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning

Game Jams as Valuable Tools for the Development of 21st-Century Skills

Envisioning Narrative Intelligence: A Creative Visual Storytelling Anthology

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