Learning in Networks of Nondeterministic Adaptive Logic Elements
Richard C. Windecker · Neural Information Processing Systems · 1987
This paper presents a model of nondeterministic adaptive automata that are constructed from simpler nondeterministic adaptive information processing elements. The first half of the paper describes the model. The second half discusses some of its significant adaptive properties using computer simulation examples. Chief among these properties is that network aggregates of the model elements can adapt appropriately when a single reinforcement channel provides the same positive or negative reinforcement signal to all adaptive elements of the network at the same time. This holds for multiple-input, multiple-output, multiple-layered, combinational and sequential networks. It also holds when some network elements are hidden in that their outputs are not directly seen by the external environment.