Creativity Support for Interactive Narrative

Summary

We are building AI-assisted tools for collaboratively understanding and generating interactive narrative scenarios. We started by creating a graph-based representation of interactive narratives, and calculating metrics to better understand their structure.

Research Topics

Interactive Narrative | Mixed-Initiative Content Creation Tools | Automated Playtesting | Metrics and Representation of Interactive Narrative Structure

Contributors

Elín Carstensdóttir | Sam Snodgrass | Erica Kleinman | Gillian M. Smith | Casper Harteveld | Magy Seif El-Nasr

Description

In the "Creativity Support for Interactive Narrative" project, we are working to make interactive narrative design tools smarter by developing AI-based understanding of scenarios in order to provide suggestions to the designer. This is known as "mixed-initiative content creation," in which a human and computer work together creatively (Liapis et. al. 2016). Interactive narrative is a new domain for this type of AI, so we are starting from the beginning, developing representations, metrics, and other building blocks for computational understanding of interactive narrative. The initial representation and metrics were presented at AIIDE 2018. The next step is to use these automated analyses to build questions, visualizations, and suggestions for designers!

We are working with the 2D interactive scenario design tool "StudyCrafter" (Harteveld et. al. 2016). It provides a graphical scripting language and easy-to-learn interface for creating playful experiences and experiments, which often involve elements of interactive narrative. We have developed a graph-based representation of its scenarios and a set of metrics to better understand their structure. By measuring attributes of the scenario and how it interacts with players, we can begin to determine how to help a designer add, change, or improve aspects of the design.

We divide the metrics into three factors, or categories: narrative structure complexity, interactive affordances, and action space. These factors are designed to measure the structural aspects of the scenarios. Some of our metrics are calculated using automated playthroughs, and some are based on metrics developed by Szilas and Ilea (2014). By examining scenarios individually using these factors, or with specific metrics, designers can augment their own insight into their scenario's strengths and weaknesses. With expressive range visualizations, K-Medoids clustering, and manual analyses, the metrics can guide and simplify manual, qualitative analysis of complex interactive narratives. In continuing work, we plan to enable AI systems to use similar insights and analyses to guide their suggestions.

To be able to calculate metrics on the interactive narratives, we need to have a computational representation that clearly defines and differentiates the scenarios. We defined four types of graph: the scene flow map, script graph, layout graph, and interaction map. These graphs each capture separate information about the scenario, enabling analysis. The graphs are generated automatically from the original StudyCrafter scenario files. The interaction map, in particular, is based on theory by Carstensdóttir et. al. (2017), and is designed to represent the potential flow of interactions between the player and the scenario. It enables visualization, as well as static and playthrough-based analysis, of the ways in which the scenario provides choices and feedback to players.

Status

In Progress

References

Carstensdóttir, Elín, Erica Kleinman, and Magy Seif El-Nasr. 2017. “Towards an Interaction Model for Interactive Narratives.” ICIDS 2017.
Harteveld, Casper, Amy Stahl, Gillian Smith, Cigdem Talgar, and Steven C. Sutherland. 2016. “Standing on the Shoulders of Citizens: Exploring Gameful Collaboration for Creating Social Experiments.” In 49th Hawaii International Conference on System Sciences, 74–83. https://doi.org/10.1109/HICSS.2016.18.
Liapis, Antonios, Gillian Smith, and Noor Shaker. 2016. “Mixed-Initiative Content Creation.” In Procedural Content Generation in Games, 195–214. Computational Synthesis and Creative Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-42716-4_11.
Szilas, Nicolas, and Ioana Ilea. 2014. “Objective Metrics for Interactive Narrative.” In Interactive Storytelling, 91–102. Lecture Notes in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-12337-0_9.

Publications

Partlan, Nathan, Elin Carstensdottir, Sam Snodgrass, Erica Kleinman, Gillian Smith, Casper Harteveld, and Magy Seif El-Nasr. 2018. “Exploratory Automated Analysis of Structural Features of Interactive Narrative.” In Proceedings of the 14th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. Edmonton, AB, Canada: The AAAI Press, Palo Alto, California.