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Humans' continual learning (CL) ability is closely related to StabilityVersus Plasticity Dilemma that describes how humans achieve ongoing learningcapacity and preservation for learned information. The notion of CL has alwaysbeen present in artificial intelligence (AI) since its births. This paperproposes a comprehensive review of CL. Different from previous reviews thatmainly focus on the catastrophic forgetting phenomenon in CL, this papersurveys CL from a more macroscopic perspective based on the Stability VersusPlasticity mechanism. Analogous to biological counterpart, "smart" AI agentsare supposed to i) remember previously learned information (informationretrospection); ii) infer on new information continuously (informationprospection:); iii) transfer useful information (information transfer), toachieve high-level CL. According to the taxonomy, evaluation metrics,algorithms, applications as well as some open issues are then introduced. Ourmain contributions concern i) rechecking CL from the level of artificialgeneral intelligence; ii) providing a detailed and extensive overview on CLtopics; iii) presenting some novel ideas on the potential development of CL.

Reviewing continual learning from the perspective of human-level intelligence