Abstract

The property inheritance network (PIN) is a dynamically controlled machine that accesses information stored in a hierarchical content addressable memory. The associative memory is implemented using adaptive resonance circuits. These circuits are monitored by a set of control neurons that become active when certain system states occur and generate signals that control a sequential search through a taxonomy of stored information. This paper reviews pertinent knowledge representation concepts and summarizes the adaptive resonance theory of Carpenter and Grossberg as it applies to the PIN. The PIN architecture and control implementation are presented and simulation results are discussed.

© 1987 Optical Society of America

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