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Human-AI Collaboration and Decision Support / Decision Optimization and Reinforcement Learning Understanding

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

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

Similar questions

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

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

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Supporting Task Switching with Reinforcement Learning

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Amortised Experimental Design and Parameter Estimation for User Models of Pointing

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Breathing Life Into Biomechanical User Models

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