Inductive knowledge acquisition in the domain of computer aided manufacturing and testing

  • Authors:

  • Martin P. Weiss;Dieter A. Mlynski

  • Affiliations:

  • Institut für Theoretische Elektrotechnik und Meβtechnik, Universität Karlsruhe (TH), D-7500 Karlsruhe, Germany;Institut für Theoretische Elektrotechnik und Meβtechnik, Universität Karlsruhe (TH), D-7500 Karlsruhe, Germany

  • Venue:
  • IEA/AIE '90 Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1
  • Year:
  • 1990

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Abstract

Inductive learning is a valuable tool for knowledge acquisition. We present a new, two-phase algorithm, Concept Agglomeration and Division of Attribute Space (CADIA), to overcome the drawbacks of conventional inductive approaches. Use of background knowledge is made by linking the attributes in a semantic net to model attributes being non-applicable to certain examples or taking on default values. This, together with the ability to generate rules of exceptions makes CADIA a powerful tool. We present concepts learned by CADIA in a subdomain of CAM/CAT, planning automatic tests for printed circuit boards, and show their relevance to knowledge engineering. Results from CADIA can give important hints at poorly structured regions of domain knowledge which have to be revised by experts. For future research, we recommend comparative studies about a “bias towards knowledge engineering”.