A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general understanding of the world. Such an agent would require the ability to continually accumulate and build upon its knowledge as it encounters new experiences. Lifelong or continual learning addresses this setting, whereby an agent faces a continual stream of problems and must strive to capture the knowledge necessary for solving each new task it encounters. If the agent is capable of accumulating knowledge in some form of compositional representation, it could then selectively reuse and combine relevant pieces of knowledge to construct novel solutions. Despite the intuitive appeal of this simple idea, the literatures on lifelong learning and compositional learning have proceeded largely separately. In an effort to promote developments that bridge between the two fields, this article surveys their respective research landscapes and discusses existing and future connections between them.

翻译：人工智能（AI）的一个主要目标是创建一个能够获得对世界普遍理解的智能体。这样的智能体需要具备在不断遇到新体验时持续积累和构建知识的能力。终身学习或持续学习正是针对这一设定，即智能体面临持续不断的问题流，并必须努力获取解决每个新任务所需的知识。如果智能体能够以某种组合表示形式积累知识，它就可以有选择性地重用和组合相关知识点来构建新颖的解决方案。尽管这一简单想法具有直觉上的吸引力，但关于终身学习和组合学习的已有研究在很大程度上是独立发展的。为了促进这两个领域之间的桥梁式进展，本文综述了它们各自的研究格局，并讨论了现有及未来的联系。