AITentive: A Toolkit to Develop RL-based Attention Management Systems
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.
Authors
- Dinara Talypova (ORCID: https://orcid.org/0000-0002-6612-8061)
- Philipp Wintersberger (ORCID: https://orcid.org/0000-0001-9287-3770)
- Alexander Lingler (ORCID: https://orcid.org/0009-0004-9439-7375)
Institutions
- TU Wien (AT)
- University of Applied Sciences Upper Austria (AT)
Publication Details
- Published
- 2024-10-11
- DOI
- https://doi.org/10.1145/3672539.3686314
- Citations
- 2
- Primary Topic
- Personal Information Management and User Behavior
- Type
- article
- Field-Weighted Citation Impact
- 2.10