Storing and generalizing multiple instances while maintaining knowledge-level parallelism

  • Authors:

  • Ronald A. Sumida;Michael G. Dyer

  • Affiliations:

  • Artificial Intelligence Laboratory, Computer Science Department, University of California, Los Angeles, CA;Artificial Intelligence Laboratory, Computer Science Department, University of California, Los Angeles, CA

  • Venue:
  • IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
  • Year:
  • 1989

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Abstract

One of the primary problems in knowledge representation and learning is determining how multiple instances of concepts should be organized and represented. Symbolic approaches, such as semantic networks, have been successful at representing structured knowledge for parallel access. However, such approaches have had difficulty organizing multiple instances for automatic generalization and efficient retrieval. Parallel distributed processing systems (PDP) appear to offer a solution to these problems. Unfortunately, current PDP models have not yet been able to satisfactorily represent complex knowledge structures and they remain sequential at the knowledge level. This paper presents an approach which stores multiple instances in ensembles of PDP units and organizes the ensembles in a semantic network for parallelism and structure. Thus, the best features of both styles of representation are obtained.