EXTEND CONNECTIONIST NETWORK MODELS TO DO SEQUENTIAL REASONING.

Y. C. Lee, H. H. Chen, G. Z. Sun, H. Y. Lee, Tom Maxwell, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Summary form only given, as follows. A neural blackboard architecture is proposed in which a high-order associative memory network serves as a global shared memory for one or more feedforward-type neural inference engines to allow the system to reason in a time-delayed, or sequential, manner. New learning algorithms are proposed to speed up slow learning and to partially alleviate the tough credit apportionment problem inherent in any multistep decision.

Original languageEnglish (US)
Title of host publicationUnknown Host Publication Title
PublisherIEEE
Pages6
Number of pages1
StatePublished - 1987

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All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Lee, Y. C., Chen, H. H., Sun, G. Z., Lee, H. Y., Maxwell, T., & Giles, C. L. (1987). EXTEND CONNECTIONIST NETWORK MODELS TO DO SEQUENTIAL REASONING. In Unknown Host Publication Title (pp. 6). IEEE.