1. Mobile User Interface Adaptation Based on Usability Reward Model and Multi-Agent Reinforcement Learning.
- Author
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Vidmanov, Dmitry and Alfimtsev, Alexander
- Subjects
MACHINE learning ,USER interfaces ,DIGITAL technology ,REINFORCEMENT learning ,COMPUTER systems ,MATHEMATICAL formulas ,REINFORCEMENT (Psychology) - Abstract
Today, reinforcement learning is one of the most effective machine learning approaches in the tasks of automatically adapting computer systems to user needs. However, implementing this technology into a digital product requires addressing a key challenge: determining the reward model in the digital environment. This paper proposes a usability reward model in multi-agent reinforcement learning. Well-known mathematical formulas used for measuring usability metrics were analyzed in detail and incorporated into the usability reward model. In the usability reward model, any neural network-based multi-agent reinforcement learning algorithm can be used as the underlying learning algorithm. This paper presents a study using independent and actor-critic reinforcement learning algorithms to investigate their impact on the usability metrics of a mobile user interface. Computational experiments and usability tests were conducted in a specially designed multi-agent environment for mobile user interfaces, enabling the implementation of various usage scenarios and real-time adaptations. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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