Knowledge Acquisition and Machine Learning. Knowledge-Based Systems

  • ID: 1767223
  • Book
  • 305 Pages
  • Elsevier Science and Technology
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For graduate-/research- level students and professors, this book integrates machine learning with knowledge acquisition to overcome the problems of building models for knowledge-based systems to maintain them successfully. It also reports on BLIP and MOBAL systems developed over the last decade, which illustrate a particular way of unifying knowledge acquisition and machine learning. Practically-orientated, theoretical skills have been used and tested in real-world applications.

  • Integrates machine learning with knowledge acquisition to overcome the problems of building models for knowledge based systems to maintain them successfully
  • Reports on BLIP and MOBAL systems that have been developed over the past 10 years, which illustrate a particular way of unifying knowledge acquisition and machine learning
  • Practically oriented--theoretical results have been used and tested in real-world applications from the start

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The Knowledge Acquisition Framework. The Knowledge Representation Environment. The Inference Im-2. The Sort Taxonomy. The Predicate Structure. Model-Driven Rule Discovery. Knowledge Revision. Concept Formation. Practical Experiences. Bibliography. Author Index. Name Index. Subject Index.
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Morik, Katharina
Wrobel, Stefan
Kietz, Jorg-Uwe
Emde, Werner
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