Life-long learning Cell Structures—continuously learning without catastrophic interference

Abstract

As an extension of on-line learning, life-long learning challenges a system which is exposed to patterns from a changing environment during its entire lifespan. An autonomous system should not only integrate new knowledge on-line into its memory, but also preserve the knowledge learned by previous interactions. Thus, life-long learning implies the fundamental Stability–Plasticity Dilemma, which addresses the problem of learning new patterns without forgetting old prototype patterns. We propose an extension to the known Cell Structures, growing Radial Basis Function-like networks, that enables them to learn their number of nodes needed to solve a current task and to dynamically adapt the learning rate of each node separately. As shown in several simulations, the resulting Life-long Learning Cell Structures posses the major characteristics needed to cope with the Stability–Plasticity Dilemma.