Smith, Gillian


A Monte Carlo Approach to Skill-Based Automated Playtesting

AAAI Conferences

In order to create well-crafted learning progressions, designers guide players as they present game skills and give ample time for the player to master those skills. However, analyzing the quality of learning progressions is challenging, especially during the design phase, as content is ever-changing. This research presents the application of Stratabots — automated player simulations based on models of players with varying sets of skills — to the human computation game Foldit. Stratabot performance analysis coupled with player data reveals a relatively smooth learning progression within tutorial levels, yet still shows evidence for improvement. Leveraging existing general gameplaying algorithms such as Monte Carlo Evaluation can reduce the development time of this approach to automated playtesting without losing predicitive power of the player model.


A Design Pattern Approach for Multi-Game Level Generation

AAAI Conferences

Existing approaches to multi-game level generation rely upon level structure to emerge organically via level fitness. In this paper, we present a method for generating levels for games in the GVGAI framework using a design pattern-based approach, where design patterns are derived from an analysis of the existing corpus of GVGAI game levels. We created two new generators: one constructive, and one search-based, and compared them to a prior existing search-based generator. Results show that our generator is comparable, even preferred, over the prior generator, especially among players with existing game experience. Our search-based generator also outperforms our constructive generator in terms of player preference.


Exploratory Automated Analysis of Structural Features of Interactive Narrative

AAAI Conferences

Analysis of interactive narrative is a complex undertaking, requiring understanding of the narrative's design, its affordances, and its impact on players. Analysis is often performed by an expert, but this is expensive and difficult for complex interactive narratives. Automated analysis of structure, the organization of interaction elements, could help augment an expert's analysis. For this purpose we developed a model consisting of a set of metrics to analyze interactive narrative structure, enabled by a novel multi-graph representation. We implemented this model for an interactive scenario authoring tool called StudyCrafter and analyzed 20 student-designed scenarios. We show that the model illuminates the structures and groupings of the scenarios. This work provides insight for manual analysis of attributes of interactive narratives and a starting point for automated design assistance.


Explainable PCGML via Game Design Patterns

arXiv.org Artificial Intelligence

Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not require hand authoring of initial content or rules. Instead, PCGML relies on existing content and black box models, which can be difficult to tune or tweak without expert knowledge. This is especially problematic when a human designer needs to understand how to manipulate their data or models to achieve desired results. We present an approach to Explainable PCGML via Design Patterns in which the design patterns act as a vocabulary and mode of interaction between user and model. We demonstrate that our technique outperforms non-explainable versions of our system in interactions with five expert designers, four of whom lack any machine learning expertise.


Generative Design for Textiles: Opportunities and Challenges for Entertainment AI

AAAI Conferences

This paper reports on two generative systems that work in the domain of textiles: the Hoopla system that generates patterns for embroidery samplers, and the Foundry system that creates foundation paper piecing patterns for quilts. Generated patterns are enacted and interpreted by the human who stitches the final product, following a long and laborious, yet entertaining and leisurely, process of stitching and sewing. The blending of digital and physical spaces, the tension between machine and human authorship, and the juxtaposition of stereotypically masculine computing with highly feminine textile crafts, leads to the opportunity for new kinds of tools, experiences, and artworks. This paper argues for the values of textiles as a domain for generative methods research, and discusses generalizable research problems that are highlighted through operating in this new domain.


The Future of Procedural Content Generation in Games

AAAI Conferences

The future of procedural content generation (PCG) lies beyond the dominant motivations of “replayability” and creating large environments for players to explore. This paper explores both the past and potential future for PCG, identifying five major lenses through which we can view PCG and its role in a game: data vs. process intensiveness, the interactive extent of the content, who has control over the generator, how many players interact with it, and the aesthetic purpose for PCG being used in the game. Using these lenses, the paper proposes several new research directions for PCG that require both deep technical research and innovative game design.


Workshops Held at the Ninth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE): A Report

AI Magazine

The Ninth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) was held October 14–18, 2013, at Northeastern University in Boston, Massachusetts. Workshops were held on the two days prior to the start of the main conference, giving attendees a chance to hold in-depth discussions on topics that complement the themes of the main conference program. This year the workshops included the First Workshop on AI and Game Aesthetics (1 day), The Second Workshop on AI in the Game Design Process (1 day), The Second International Workshop on Musical Metacreation (2 day), The Sixth Workshop on Intelligent Narrative Technologies (2 day).


Workshops Held at the Ninth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE): A Report

AI Magazine

The Ninth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) was held October 14–18, 2013, at Northeastern University in Boston, Massachusetts. Workshops were held on the two days prior to the start of the main conference, giving attendees a chance to hold in-depth discussions on topics that complement the themes of the main conference program. This year the workshops included the First Workshop on AI and Game Aesthetics (1 day), The Second Workshop on AI in the Game Design Process (1 day), The Second International Workshop on Musical Metacreation (2 day), The Sixth Workshop on Intelligent Narrative Technologies (2 day).


Endless Web

AAAI Conferences

Endless Web is a game where a procedural content generator is deeply integrated into the mechanics, dynamics, and aesthetics. The game involves players exploring the generative space of its built-in content generator, by making decisions that influence the parameters to the generator. Players must build strategies around the choices they are making to ensure that levels are at an appropriate level of challenge for them, while simultaneously exploring to discover goals hidden in layers of the generative space.  


Reports on the Fourth Artificial Intelligence for Interactive Digital Entertainment Conference Workshops

AI Magazine

The Seventh Artificial Intelligence for Interactive Digital Entertainment Conference (AIIDE-11) was held October 11–14, 2011 at Stanford University, Stanford, California. Two one-day workshops were held on October 11: Artificial Intelligence in the Game Design Process, and Intelligent Narrative Technologies. The highlights of each workshop are presented in this report.