HCI.TOPHCI, made easy

arrow_backBack to questions View categoryarrow_forward

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

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

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

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 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

Related papers

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

Jiayi Eurus Zhang, Bernhard Hilpert, Joost Broekens

Supporting Task Switching with Reinforcement Learning

Alexander Lingler, Dinara Talypova, Jussi P. P. Jokinen

Amortised Experimental Design and Parameter Estimation for User Models of Pointing

Antti Keurulainen, Isak Rafael Westerlund, Oskar Keurulainen

Breathing Life Into Biomechanical User Models

Aleksi Ikkala, Florian Fischer, Markus Klar

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

Zhi Li, Maozheng Zhao, Dibyendu Das

Rediscovering Affordance: A Reinforcement Learning Perspective

Yi-Chi Liao, Kashyap Todi, Aditya Acharya

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

Seungwon Do, Minsuk Chang, Byungjoo Lee

Touchscreen Typing As Optimal Supervisory Control

Jussi P. P. Jokinen, Aditya Acharya, Mohammad Uzair

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

Hee-Seung Moon, Jiwon Seo

Adapting User Interfaces with Model-based Reinforcement Learning

Kashyap Todi, Gilles Bailly, Luis Leiva