Title :
Distributed knowledge representation in fully connected networks
Author_Institution :
Los Alamos Nat. Lab., NM, USA
Abstract :
Fully-connected binary networks, in addition to implementing content addressable memories, have been shown to be capable of encoding arbitrary limit cycles using synchronous dynamics. A stochastic knowledge representation paradigm is proposed, and a way to encode this knowledge form into cycles in fully-connected networks is described. This new representation format stores information in a truly distributed manner across the network, as opposed to previous schemes which store one knowledge atom per neuron
Keywords :
content-addressable storage; encoding; graph theory; knowledge representation; limit cycles; perceptrons; stochastic processes; content addressable memories; distributed knowledge representation; encoding; fully-connected binary networks; graph knowledge; information storage; limit cycles; perceptron networks; stochastic knowledge representation; synchronous dynamics; Artificial intelligence; Associative memory; Biological information theory; Encoding; Intelligent networks; Knowledge representation; Laboratories; Limit-cycles; Pattern analysis; Stochastic processes;
Conference_Titel :
Intelligence and Systems, 1996., IEEE International Joint Symposia on
Conference_Location :
Rockville, MD
Print_ISBN :
0-8186-7728-7
DOI :
10.1109/IJSIS.1996.565055
