持续学习:一个系统的文献综述
Qinwen Yang1, Liyuan Wang2, Joerg Wicker1
1School of Computer Science, University of Auckland, 38 Princes Street, Auckland, 1010, New Zealand.
概括
智能系统需要持续学习 (CL) 才能在不遗忘的情况下适应. 本综述分析了189篇论文 (2010-2025年),为灾难性遗忘提供了新的见解,以及在CL中未来的研究方向.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 智能系统需要不断适应不断变化的数据流.
- 持续学习 (CL) 使系统能够在没有先前数据的情况下顺序学习.
- 灾难性遗忘 (CF) 是一个主要的挑战,新学习会损害旧的知识.
研究的目的:
- 提供从2010年到2025年持续学习 (CL) 研究的系统文献综述 (SLR).
- 通过统一分类和探索灾难性遗忘 (CF) 的原因,解决现有调查的局限性.
- 在经验测试之外,为CL挑战推进一个理论框架.
主要方法:
- 在五个主要数据库中进行自动搜索.
- 在13个主要的学术场所进行手动搜索.
- 策划了189篇关于持续学习的同行评审论文的数据集.
主要成果:
- 介绍了对CL研究趋势和方法的全面概述.
- 为CL方法和实验环境开发了精细的分类学.
- 分析了灾难性遗忘 (CF) 的结构和功能原因.
- 识别了在CL中开放的理论问题和新兴的研究方向.
结论:
- 该审查为CL提供了一个统一的视角,这对于开发弹性AI至关重要.
- 了解CF的原因对于推进CL方法至关重要.
- 这些发现指导了未来的研究,以更强大,更适应的智能系统.
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