Mobile User Interface Adaptation Based on Usability Reward Model and Multi-Agent Reinforcement Learning

被引:0
|
作者
Vidmanov, Dmitry [1 ]
Alfimtsev, Alexander [1 ]
机构
[1] Bauman Moscow State Tech Univ, Informat Syst & Telecommun, Moscow 105005, Russia
关键词
deep learning; reinforcement learning; multi-agent systems; adaptive systems; mobile user interface; usability; user experience; LEVEL;
D O I
10.3390/mti8040026
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页数:21
相关论文
共 50 条
  • [1] Multi-Agent Reinforcement Learning with Reward Delays
    Zhang, Yuyang
    Zhang, Runyu
    Gu, Yuantao
    Li, Na
    [J]. LEARNING FOR DYNAMICS AND CONTROL CONFERENCE, VOL 211, 2023, 211
  • [2] Direct reward and indirect reward in multi-agent reinforcement learning
    Ohta, M
    [J]. ROBOCUP 2002: ROBOT SOCCER WORLD CUP VI, 2003, 2752 : 359 - 366
  • [3] Direct reward and indirect reward in multi-agent reinforcement learning
    [J]. Ohta, M. (ohta@carc.aist.go.jp), (Springer Verlag):
  • [4] Plan-based reward shaping for multi-agent reinforcement learning
    Devlin, Sam
    Kudenko, Daniel
    [J]. KNOWLEDGE ENGINEERING REVIEW, 2016, 31 (01): : 44 - 58
  • [5] Rationality of reward sharing in multi-agent reinforcement learning
    Kazuteru Miyazaki
    Shigenobu Kobayashi
    [J]. New Generation Computing, 2001, 19 : 157 - 172
  • [6] Rationality of reward sharing in multi-agent reinforcement learning
    Miyazaki, K
    Kobayashi, S
    [J]. NEW GENERATION COMPUTING, 2001, 19 (02) : 157 - 172
  • [7] Individual Reward Assisted Multi-Agent Reinforcement Learning
    Wang, Li
    Zhang, Yupeng
    Hu, Yujing
    Wang, Weixun
    Zhang, Chongjie
    Gao, Yang
    Hao, Jianye
    Lv, Tangjie
    Fan, Changjie
    [J]. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162, 2022,
  • [8] Autonomous learning of reward distribution for each agent in multi-agent reinforcement learning
    Shibata, K
    Ito, K
    [J]. INTELLIGENT AUTONOMOUS SYSTEMS 6, 2000, : 495 - 502
  • [9] Emotion-Based Heterogeneous Multi-agent Reinforcement Learning with Sparse Reward
    Fang, Baofu
    Ma, Yunting
    Wang, Zaijun
    Wang, Hao
    [J]. Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence, 2021, 34 (03): : 223 - 231
  • [10] Probabilistic Reward-Based Reinforcement Learning for Multi-Agent Pursuit and Evasion
    Zhang, Bo-Kun
    Hu, Bin
    Chen, Long
    Zhang, Ding-Xue
    Cheng, Xin-Ming
    Guan, Zhi-Hong
    [J]. PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 3352 - 3357