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

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. (C) 2001 Elsevier Science Ltd. All rights reserved.