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A RL Based Model for Improving Human Task Management Performance
Muddasar Naeem, Valeriano Fabris, Antonio Coronato
258 - 265
10.3233/AISE220051
Research Article
Ambient Intelligence and Smart Environments
Volume 31: Workshops at 18th International Conference on Intelligent Environments (IE2022)
Abstract

This paper discusses an Reinforcement Learning (RL) based system to improve human performance in task selection and management by incorporating various factors. A simulated task management environment of a coaching center is considered. Different factors includes task urgency, task status, and task importance as well as which task to attend to next, and that an even more important factor of task management is the capability to avoid low importance tasks. Five algorithms such as: Boltzman Sampling, Epsilon-decreasing, Random, Softmax, and Thompson Sampling have been used in experiments.

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