ACQUIRING RULE SETS AS A PRODUCT OF LEARNING IN A LOGICAL NEURAL ARCHITECTURE

Envisioning neural networks as systems that learn rules calls forth th e verification issues already being studied in knowledge-based systems engineering, and complicates these with neural-network concepts such as nonlinear dynamics and distributed memories, We show that the issue s can be clarified and the learned rules visualized symbolically by fo rmalizing the semantics of rule-learning in the mathematical language of two-valued predicate logic, We further show that this can, at least in some cases, be done with a fairly simple logical model, We illustr ate this,vith a combination of two example neural-network architecture s, LAPART, designed to learn rules as logical inferences from binary d ata patterns, and the stack interval network, which converts real-valu ed data into binary patterns that preserve the semantics of the orderi ng of real values, We discuss the significance of the formal model in facilitating the analysis of the underlying logic of rule-learning and numerical data representation, We provide examples to illustrate the formal model, with the combined stack interval/LAPART networks extract ing rules from numerical data.