Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems

Dekang Zhu; Qianyi Bu; Zhongpan Zhu; Yujie Zhang; Zhipeng Wang

doi:10.3389/fnbot.2024.1385778

Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems

Front Neurorobot. 2024 Apr 5:18:1385778. doi: 10.3389/fnbot.2024.1385778. eCollection 2024.

Authors

Dekang Zhu¹, Qianyi Bu², Zhongpan Zhu^{1

3}, Yujie Zhang¹, Zhipeng Wang¹

Affiliations

¹ College of Electronic and Information Engineering, Tongji University, Shanghai, China.
² College of Science and Engineering, University of Glasgow, Glasgow, United Kingdom.
³ College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China.

Abstract

The combination of lifelong learning algorithms with autonomous intelligent systems (AIS) is gaining popularity due to its ability to enhance AIS performance, but the existing summaries in related fields are insufficient. Therefore, it is necessary to systematically analyze the research on lifelong learning algorithms with autonomous intelligent systems, aiming to gain a better understanding of the current progress in this field. This paper presents a thorough review and analysis of the relevant work on the integration of lifelong learning algorithms and autonomous intelligent systems. Specifically, we investigate the diverse applications of lifelong learning algorithms in AIS's domains such as autonomous driving, anomaly detection, robots, and emergency management, while assessing their impact on enhancing AIS performance and reliability. The challenging problems encountered in lifelong learning for AIS are summarized based on a profound understanding in literature review. The advanced and innovative development of lifelong learning algorithms for autonomous intelligent systems are discussed for offering valuable insights and guidance to researchers in this rapidly evolving field.

Keywords: algorithm; artificial intelligence; autonomous intelligent systems; future perspectives; lifelong learning.

Publication types

Review

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported in part by the National Key Research and Development Program of China (t 2021YFE0193900 and 2020AAA0108901), in part by the Fundamental Research Funds for the Central Universities (22120220642).