HCI.TOPHCI, made easy

arrow_backBack to questions View categoryarrow_forward

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

How can deep reinforcement learning combined with vector databases optimize user interest recognition and video summarization query efficiency?

helpResearch questionDecision Optimization and Reinforcement Learning Understanding

How can deep reinforcement learning combined with vector databases optimize user interest recognition and video summarization query efficiency?

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

Similar questions

helpResearch questionDecision Optimization and Reinforcement Learning Understanding

How can reinforcement learning optimize users' attention switching in multitasking environments?

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

helpResearch questionDecision Optimization and Reinforcement Learning Understanding

How does AITentive simplify the design and development of attention management systems (AMS)?

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

helpResearch questionDecision Optimization and Reinforcement Learning Understanding

Can AITentive effectively support multi-scenario AMS development and improve multitasking performance?

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

helpResearch questionDecision Optimization and Reinforcement Learning Understanding

How can natural language enhance video camera interaction capabilities to customize tasks based on user interests?

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

helpResearch questionDecision Optimization and Reinforcement Learning Understanding

How can multimodal language models generate lightweight task models to meet user needs in edge computing environments?

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

helpResearch questionDecision Optimization and Reinforcement Learning Understanding

How can mechanisms from attribution theory of emotion (e.g., goal relevance, novelty, goal conduciveness, controllability) be integrated into reinforcement learning to simulate emotional change?

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

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