1 citations · 3 across the 8 of their papers we have counts for
9 papers
Player Perceptions of Generative AI in Games: A Steam Review Analysis
Mahsa Bazzaz, Seth Cooper
The rapid adoption of generative AI in game development has created large discussions among players, yet little empirical work has examined how players actually perceive AI-generat…
Geometric Signatures of Reasoning: A Spectral Perspective on Task Hardness
Aria Masoomi, Mahsa Bazzaz, Adel Javanmard +1
Chain-of-thought (CoT) reasoning enables large language models (LLMs) to solve complex problems by generating intermediate reasoning steps. While much attention has been paid to th…
Playing the Imitation Game: How Perceived Generated Content Shapes Player Experience
Mahsa Bazzaz, Seth Cooper
With the fast progress of generative AI in recent years, more games are integrating generated content, raising questions regarding how players perceive and respond to this content.…
Level Generation with Constrained Expressive Range
Mahsa Bazzaz, Seth Cooper
Expressive range analysis is a visualization-based technique used to evaluate the performance of generative models, particularly in game level generation. It typically employs two…
Analysis of Robustness of a Large Game Corpus
Mahsa Bazzaz, Seth Cooper
Procedural content generation via machine learning (PCGML) in games involves using machine learning techniques to create game content such as maps and levels. 2D tile-based game le…
Analysis of Uncertainty in Procedural Maps in Slay the Spire
Mahsa Bazzaz, Seth Cooper
This work investigates the role of uncertainty in Slay the Spire using an information-theoretic framework. Focusing on the entropy of game paths (which are based on procedurally-ge…
Guided Game Level Repair via Explainable AI
Mahsa Bazzaz, Seth Cooper
Procedurally generated levels created by machine learning models can be unsolvable without further editing. Various methods have been developed to automatically repair these levels…
Controllable Game Level Generation: Assessing the Effect of Negative Examples in GAN Models
Mahsa Bazzaz, Seth Cooper
Generative Adversarial Networks (GANs) are unsupervised models designed to learn and replicate a target distribution. The vanilla versions of these models can be extended to more c…
Active Learning for Classifying 2D Grid-Based Level Completability
Mahsa Bazzaz, Seth Cooper
Determining the completability of levels generated by procedural generators such as machine learning models can be challenging, as it can involve the use of solver agents that ofte…