基于可用性奖励模型和多代理强化学习的移动用户界面调整

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Multimodal Technologies and Interaction Pub Date : 2024-03-24 DOI:10.3390/mti8040026
Dmitry Vidmanov, Alexander Alfimtsev
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引用次数: 0

摘要

如今,强化学习已成为自动调整计算机系统以适应用户需求的任务中最有效的机器学习方法之一。然而,将这项技术应用到数字产品中需要解决一个关键难题:确定数字环境中的奖励模型。本文提出了多代理强化学习中的可用性奖励模型。本文详细分析了用于衡量可用性指标的著名数学公式,并将其纳入可用性奖励模型。在可用性奖励模型中,任何基于神经网络的多代理强化学习算法都可以作为底层学习算法。本文介绍了一项使用独立强化学习算法和行动者批判强化学习算法的研究,以探讨它们对移动用户界面可用性指标的影响。计算实验和可用性测试是在专门为移动用户界面设计的多代理环境中进行的,该环境可以实现各种使用场景和实时调整。
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Mobile User Interface Adaptation Based on Usability Reward Model and Multi-Agent Reinforcement Learning
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.
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来源期刊
Multimodal Technologies and Interaction
Multimodal Technologies and Interaction Computer Science-Computer Science Applications
CiteScore
4.90
自引率
8.00%
发文量
94
审稿时长
4 weeks
期刊最新文献
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