持续学习的全面调查:理论,方法和应用
IEEE transactions on pattern analysis and machine intelligence
|February 26, 2024
概括
持续学习使人工智能系统能够通过随着时间的推移获得知识来适应. 这项调查解决了灾难性遗忘问题,并探索了在现实世界中应用稳定,适应性AI的方法.
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:
- 智能系统需要终身的知识获取和更新,以适应现实世界的动态.
- 持续学习对于适应性AI发展至关重要,但受到灾难性遗忘的挑战.
- 最近的进展显著扩大了对持续学习的理解和应用.
研究的目的:
- 提供关于持续学习的全面调查.
- 为了弥合基本设置,理论基础,代表性方法和实际应用.
- 促进对持续学习的未来研究.
主要方法:
- 总结一般目标:稳定性-可塑性权衡和任务内/任务间的概括性.
- 介绍持续学习策略的最先进的分类学.
- 分析方法如何解决各种应用中的挑战.
主要成果:
- 持续学习的目标是保持旧知识和获取新信息之间的平衡.
- 一个结构化的分类学分类和分析各种持续学习方法.
- 方法在不同的应用环境中对其有效性进行评估.
结论:
- 对持续学习的整体观点对于推动该领域的发展至关重要.
- 解决灾难性遗忘问题和确保通用性是关键目标.
- 这项调查为适应性人工智能系统的未来探索提供了基础.
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