Cooperative Interaction of Self-Interested Neuron-Like Processing Units.

摘要

This report describes progress made in the development of connectionist learning methods permitting networks to learn when they cannot be provided with training information of the high quality required by supervised-learning methods. These methods can permit the application of adaptive connectionist networks to tasks involving complex dynamical behavior and high degrees of uncertainty. A method for training layered networks to perform nonlinear pattern recognition and associative memory tasks was refined. The neuron-like units making up these networks learn on the basis of feedback that evaluates behavior but does not specify desired output or directly provide error information. We report how this method is related to gradient-following methods, how its learning rate can be improved, and argue that this method is biologically plausible. A generalized theory of supervised learning was developed, in which training information comes in the form of constraints instead of specifications of desired network outputs. This approach was illustrated by using it to train a simulated multi-jointed manipulator to perform sequences of reaching tasks. Progress was made in the development of reinforcement learning methods for control of dynamical systems. Keywords: Adaptive networks, Neural computing, Stochastic learning automata, Cooperative computing, Artificial intelligence. (aw)