Search Results for author:
Found 18 papers, 4 papers with code
AgentForge: A Flexible Low-Code Platform for Reinforcement Learning Agent Design
To tackle these challenges, the paper presents AgentForge, a flexible low-code platform to optimize any parameter set across an RL system.
Graph4GUI: Graph Neural Networks for Representing Graphical User Interfaces
Present-day graphical user interfaces (GUIs) exhibit diverse arrangements of text, graphics, and interactive elements such as buttons and menus, but representations of GUIs have not kept up.
EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning
From a visual perception perspective, modern graphical user interfaces (GUIs) comprise a complex graphics-rich two-dimensional visuospatial arrangement of text, images, and interactive objects such as buttons and menus.
Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception
The model reproduces a larger number of known empirical phenomena than previous models, in particular: (1) the effect of the time to arrival of an approaching vehicle on whether the pedestrian accepts the gap, the effect of the vehicle's speed on both (2) gap acceptance and (3) pedestrian timing of crossing in front of yielding vehicles, and (4) the effect on this crossing timing of the stopping distance of the yielding vehicle.
Modeling human road crossing decisions as reward maximization with visual perception limitations
Interestingly, our model's decisions are sensitive to not only the time gap, but also the speed of the approaching vehicle, something which has been described as a "bias" in human gap acceptance behavior.
Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques
Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives.
Rediscovering Affordance: A Reinforcement Learning Perspective
Affordance refers to the perception of possible actions allowed by an object.
Adapting User Interfaces with Model-based Reinforcement Learning
Adapting an interface requires taking into account both the positive and negative effects that changes may have on the user.
Model-based Reinforcement Learning
reinforcement-learning
+2
Improving Artificial Teachers by Considering How People Learn and Forget
The paper presents a novel model-based method for intelligent tutoring, with particular emphasis on the problem of selecting teaching interventions in interaction with humans.
Understanding Visual Saliency in Mobile User Interfaces
For graphical user interface (UI) design, it is important to understand what attracts visual attention.
Human Strategic Steering Improves Performance of Interactive Optimization
To verify this hypothesis, that humans steer and are able to improve performance by steering, we designed a function optimization task where a human and an optimization algorithm collaborate to find the maximum of a 1-dimensional function.
GRIDS: Interactive Layout Design with Integer Programming
Grid layouts are used by designers to spatially organise user interfaces when sketching and wireframing.
Hierarchical Reinforcement Learning as a Model of Human Task Interleaving
The results support hierarchical RL as a plausible model of task interleaving.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Inferring Cognitive Models from Data using Approximate Bayesian Computation
An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data.
Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input
However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the two separately.
Fast and Robust Hand Tracking Using Detection-Guided Optimization
In the optimization step, a novel objective function combines the detected part labels and a Gaussian mixture representation of the depth to estimate a pose that best fits the depth.
Real-Time Hand Tracking Using a Sum of Anisotropic Gaussians Model
In this paper, we propose a new approach that tracks the full skeleton motion of the hand from multiple RGB cameras in real-time.
