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

被引：0

作者：

Lingler, Alexander ^{[1
]}

Talypova, Dinara ^{[1
]}

Wintersberger, Philipp ^{[1
,2
]}

机构：

[1] Univ Appl Sci Upper Austria, Digital Media Lab, Hagenberg, Austria

[2] TU Wien, Vienna, Austria

来源：

PROCEEDINGS OF THE 37TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, UIST ADJUNCT 2024 | 2024年

基金：

奥地利科学基金会;

关键词：

Tasks/Interruptions/Notifcation; Programming/Development Support; Information Seeking & Search; Machine Learning; PERFORMANCE;

D O I：

10.1145/3672539.3686314

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In today's fast-paced world, multitasking is common and affects productivity, decision-making, and cognition. Understanding its complexities is crucial for improving well-being, efficiency, and task management. Attention management systems optimize notification and interruption timings. This work introduces AITentive, an open-source Unity3D toolkit for multitasking research and developing attention management systems with reinforcement learning. The toolkit offers customizable tasks, built-in measurements, and a uniform interface for adding tasks, using Unity ML agents to develop and train attention management systems based on user models.

引用

页数：3

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