7 citations · 13 across the 7 of their papers we have counts for
7 papers
Strategic Gaze: Attention Allocation and Transition Patterns Across Functional Areas of Interest by Gameplay Outcome
Yufei Cao, Penny Sweetser
Video games present players with complex, spatially distributed information across interface elements, with attention shaped by visual features and task goals. Eye tracking provide…
Signals of Success and Struggle: Early Prediction and Physiological Signatures of Human Performance across Task Complexity
Yufei Cao, Penny Sweetser, Ziyu Chen +1
User performance is crucial in interactive systems, capturing how effectively users engage with task execution. Prospectively predicting performance enables the timely identificati…
Flow-Enabled Generalization to Human Demonstrations in Few-Shot Imitation Learning
Runze Tang, Penny Sweetser
Imitation Learning (IL) enables robots to learn complex skills from demonstrations without explicit task modeling, but it typically requires large amounts of demonstrations, creati…
Large Language Models and Video Games: A Preliminary Scoping Review
Penny Sweetser
Large language models (LLMs) hold interesting potential for the design, development, and research of video games. Building on the decades of prior research on generative AI in game…
Atari-5: Distilling the Arcade Learning Environment down to Five Games
Matthew Aitchison, Penny Sweetser, Marcus Hutter
The Arcade Learning Environment (ALE) has become an essential benchmark for assessing the performance of reinforcement learning algorithms. However, the computational cost of gener…
Learning to Deceive in Multi-Agent Hidden Role Games
Matthew Aitchison, Lyndon Benke, Penny Sweetser
Deception is prevalent in human social settings. However, studies into the effect of deception on reinforcement learning algorithms have been limited to simplistic settings, restri…
DNA: Proximal Policy Optimization with a Dual Network Architecture
Matthew Aitchison, Penny Sweetser
This paper explores the problem of simultaneously learning a value function and policy in deep actor-critic reinforcement learning models. We find that the common practice of learn…