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.
Hamker
F H
FH
California Institute of Technology, Division of Biology, Pasadena, CA 91125, USA.
D000465:Algorithms; D001185:Artificial Intelligence; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity