An adaptive, distributed learning system based on the immune system

The immune system enables human survival of infection and disease; when the system fails to work, or is defeated by a particular infection, human life is put at risk. As such it is one of the most important biological mechanisms humans possess. However, little attention has been paid to computer systems which use the immune system as their biological metaphor. In this paper we describe a learning system which is based on both the genetic mechanisms used to construct antibodies and on the influence of the immune network (which acts as a reinforcement memory). This unique combination results in a system which is self-organising, possesses no central controller, uses one-shot learning, possesses an explicit representation of what it has learnt and can forget little used information. This system is illustrated on a simple naughts and crosses (tic-tac-toe) application.