Continuous Reinforced Snap-Drift Learning in a Neural Architecture for Proxylet Selection in Active Computer Networks

A new continuous learning method is used to optimise the selection of services in response to user requests
in an active computer network simulation environment. The learning is an enhanced version of the ‘snap-drift’
algorithm, which employs the complementary concepts of fast, minimalist (snap) learning and slower drift (towards the
input patterns) learning, in a non-stationary environment where new patterns arrive continually. Snap is based on
Adaptive Resonance Theory, and drift on Learning Vector Quantisation. The new algorithm swaps its learning style
between these two self-organisational modes when declining performance is detected, but maintains the same learning
mode during episodes of improved performance. Performance updates occur at the end of each epoch. Reinforcement is
implemented by enabling learning on any given pattern with a probability that increases linearly with declining
performance. This method, which is capable of rapid re-learning, is used in the design of a modular neural network
system: Performance-guided Adaptive Resonance Theory (P-ART). Simulations involving a requirement to
continuously adapt to make appropirate decisions within a BT active computer network environment, demonstrate the
learning is stable, and able to discover alternative solutions in rapid response to new performance requirements or
significant changes in the stream of input patterns.

Citation:
Palmer-Brown, D.; Lee, S.W. (2005). “Continuous Reinforced Snap-Drift Learning in a Neural Architecture for Proxylet Selection in Active Computer Networks.” International Journal on Simulation: Systems, Science and Technology, 6 (9) 11-21..