  Humans' continual learning (CL) ability is closely related to Stability
Versus Plasticity Dilemma that describes how humans achieve ongoing learning
capacity and preservation for learned information. The notion of CL has always
been present in artificial intelligence (AI) since its births. This paper
proposes a comprehensive review of CL. Different from previous reviews that
mainly focus on the catastrophic forgetting phenomenon in CL, this paper
surveys CL from a more macroscopic perspective based on the Stability Versus
Plasticity mechanism. Analogous to biological counterpart, "smart" AI agents
are supposed to i) remember previously learned information (information
retrospection); ii) infer on new information continuously (information
prospection:); iii) transfer useful information (information transfer), to
achieve high-level CL. According to the taxonomy, evaluation metrics,
algorithms, applications as well as some open issues are then introduced. Our
main contributions concern i) rechecking CL from the level of artificial
general intelligence; ii) providing a detailed and extensive overview on CL
topics; iii) presenting some novel ideas on the potential development of CL.
