Foreseeing Meaningful Choices

Abstract A choice positively contributes to a player's sense of agency when it leads to meaningfully different content. We shed light on what a player may consider meaningfully different by developing a formalism for interactive stories in terms of the change in situational content across choices. We hypothesized that a player will feel a higher sense of agency when making a choice if they foresee the available actions lead to meaningfully different states. We experimentally tested our formalism's ability to characterize choices that elicit a higher sense of agency and present evidence that supports our claim. Study participants (n = 88) played a choose-your-ownadventure game and reported a higher sense of agency when faced with choices that differed in situational content over choices that didn't, despite these choices differing in non-situational ways. We contend our findings are a step toward principled approaches to the design of interactive stories that target specific cognitive and affective states

Teaching and Generative AI

With the rapid development of generative AI, teachers are experiencing a new pedagogical challenge, one that promises to forever change the way we approach teaching and learning. As a response to this unprecedented teaching context, this collection—Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions—provides interdisciplinary teachers, librarians, and instructional designers with practical and thoughtful pedagogical resources for navigating the possibilities and challenges of teaching in an AI era. Because our goal with this edited collection is to present nuanced discussions of AI technologies across disciplines, the chapters collectively acknowledge or explore both possibilities and tensions—including the strengths, limitations, ethical considerations, and disciplinary potential and challenges—of teaching in an AI era. As such, the authors in this collection do not simply praise or criticize AI, but thoughtfully acknowledge and explore its complexities within educational settings

SCOPE: Selective Cross-Validation over Parameters for Elo

It is crucial to develop reusable methods for explaining and evaluating esports given their popularity and diversity. Quantifying skill in an esport has the potential to improve win prediction, matchmaking, and storytelling for these games. Arpad Elo’s skill modeling system for chess has been adapted to many games and sports. In each instance, the modeler is challenged with tuning parameters to optimize for some metric, usually accuracy. Often these approaches are one-off and lack consistency. We propose SCOPE, a framework that uses grid search cross-validation to select optimal parameters for Elo based on accuracy, calibration, or log loss. We demonstrate this method on a season of Call of Duty World League, a first-person shooter esport, and we demonstrate comparable performance to other more complex, state-of-the-art methods

Evolving Interactive Narrative Worlds

An interactive narrative is bound by the context of the world where its story takes place. However, most work in interactive narrative generation takes its story world design and mechanics as given, which abdicates a large part of story generation to an external world designer. In this paper, we close the story world design gap with an evolutionary search framework for generating interactive narrative worlds and mechanics. Our framework finds story world designs that accommodate multiple distinct player roles. We evaluate our system with an action agreement ratio analysis that shows worlds generated by our framework provide a greater number of in-role action opportunities compared to story worlds randomly sampled from the generative space

Game System Models: Toward Semantic Foundations for Technical Game Analysis, Generation, and Design

Game system models introduce abstractions over games in order to support their analysis, generation, and design. While excellent, models to date leave tacit what they abstract over, why they are ontologically adequate, and how they would be realized in the engine underlying the game. In this paper we model these abstraction gaps via the first-order modal mu-calculus. We use it to reify the link between engines to our game interaction model, a player-computer interaction framework grounded in the Game Ontology Project. Through formal derivation and justification, we contend our work is a useful code studies perspective that affords better understanding the semantics underlying game system models in general

The Story So Far on Narrative Planning

Narrative planning is the use of automated planning to construct, communicate, and understand stories, a form of information to which human cognition and enaction is pre-disposed. We review the narrative planning problem in a manner suitable as an introduction to the area, survey different plan-based methodologies and affordances for reasoning about narrative, and discuss open challenges relevant to the broader AI community