Direction: Human-AI Collaboration and Decision Support

Decision Optimization and Reinforcement Learning Understanding

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2024

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16 items

All Research questions Practical problems

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Users frequently switching attention during multitasking achieve low efficiency, reducing productivity.

UIST '24AITentive: A Toolkit to Develop Reinforcement Learning-based Attention Management Systems

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Users struggle to quickly obtain video summary content aligned with their interests using existing technologies.

UbiComp '24ChatCam: Embracing LLMs for Contextual Chatting-to-Camera with Interest-Oriented Video Summarization

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Users' dynamic emotional changes are difficult to predict in real time and explain effectively.

CHI '24Simulating Emotions With an Integrated Computational Model of Appraisal and Reinforcement Learning

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Users in high-stress multitasking situations easily reduce efficiency or make errors due to frequent task switching.

CHI '24Supporting Task Switching with Reinforcement Learning

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Smart cities' demand for efficient communication networks cannot be met by ground networks alone.

UbiComp '23Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for Resource Management in Space-Air-Ground Integrated Networks

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Drones are prone to instability or damage during landing due to ground effect and noise interference.

UbiComp '23SmoothLander: A Quadrotor Landing Control System with Smooth Trajectory Guarantee Based on Reinforcement Learning

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

User model parameter estimation requires large amounts of user data, making the process expensive and time-consuming.

CHI '23Amortised Experimental Design and Parameter Estimation for User Models of Pointing

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Traditional user models cannot realistically simulate human interaction with physical devices or interfaces.

UIST '22Breathing Life Into Biomechanical User Models

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Users easily make errors when selecting targets on small touchscreen devices, reducing efficiency.

CHI '22Select or Suggest? Reinforcement Learning-based Method for High-Accuracy Target Selection on Touchscreens

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Users struggle to intuitively understand available functions when first using new interfaces or misleading designs.

CHI '22Rediscovering Affordance: A Reinforcement Learning Perspective

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Urban delivery demand is surging; existing methods struggle to efficiently handle time constraints and profit optimization.

UbiComp '21A City-Wide Crowdsourcing Delivery System with Reinforcement Learning

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Existing sleep intervention tools rely on overly simplistic reminders with limited effectiveness, failing to help users truly achieve sleep goals.

UbiComp '21Effects of Intervening Backwards from Goals to Improve Sleep Habits

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Pointing tasks in user interfaces lack realistic dynamic interaction model support.

CHI '21A Simulation Model of Intermittently Controlled Point-and-Click Behaviour

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Users often reduce efficiency during touchscreen input due to visual conflict and error correction.

CHI '21Touchscreen Typing As Optimal Supervisory Control

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Haptic guidance systems may conflict with user intent, reducing comfort and operational effectiveness.

CHI '21Optimal Action-based or User Prediction-based Haptic Guidance: Can You Do Even Better?

lightbulbPractical problemDecision Optimization and Reinforcement Learning Understanding

Randomly adapting UIs easily confuse users or impose high learning costs.

CHI '21Adapting User Interfaces with Model-based Reinforcement Learning

Adjacent categories

Human-AI Collaboration and Decision Support

Decision Optimization and Reinforcement Learning Understanding

All questions

Related papers

Simulating Emotions With an Integrated Computational Model of Appraisal and Reinforcement Learning

Supporting Task Switching with Reinforcement Learning

Amortised Experimental Design and Parameter Estimation for User Models of Pointing

Breathing Life Into Biomechanical User Models

Select or Suggest? Reinforcement Learning-based Method for High-Accuracy Target Selection on Touchscreens

Rediscovering Affordance: A Reinforcement Learning Perspective

A Simulation Model of Intermittently Controlled Point-and-Click Behaviour

Touchscreen Typing As Optimal Supervisory Control

Optimal Action-based or User Prediction-based Haptic Guidance: Can You Do Even Better?

Adapting User Interfaces with Model-based Reinforcement Learning

Adjacent categories

Human-AI Collaborative Decision-Making and Advice Adoption

Human-AI Collaborative Optimization and Preference Alignment

Embodied AI Collaboration and Intent Understanding

AI Latency Inference and Responsibility Allocation

Human-AI Co-Adaptation and Capability Augmentation

Human-AI Collaborative System Testing and Evaluation