Knowledge Representation and Organization in Machine Learning

1. Front Matter

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2. Explanation: A source of guidance for knowledge representation

   • William R. Swartout, Stephen W. Smoliar

   Pages 1-16

3. (Re)presentation issues in second generation expert systems

   • Walter Van de Velde

   Pages 17-49

4. Some aspects of learning and reorganization in an analogical representation

   • Michael Mohnhaupt, Bernd Neumann

   Pages 50-64

5. A knowledge-intensive learning system for document retrieval

   • Roy Rada, Hafedh Mili

   Pages 65-87

6. Constructing expert systems as building mental models or toward a cognitive ontology for expert systems

   • David Littman

   Pages 88-106

7. Sloppy modeling

   • Katharina Morik

   Pages 107-134

8. The central role of explanations in disciple

   • Yves Kodratoff, Gheorghe Tecuci

   Pages 135-147

9. An inference engine for representing multiple theories

   • Werner Emde

   Pages 148-176

10. The acquisition of model-knowledge for a model-driven machine learning approach

    • Sabine Thieme

    Pages 177-191

11. Using attribute dependencies for rule learning

    • Maarten W. van Someren

    Pages 192-210

12. Learning disjunctive concepts

    • Michel Manago, Jim Blythe

    Pages 211-230

13. The use of analogy in incremental SBL

    • Christel Vrain, Yves Kodratoff

    Pages 231-246

14. Knowledge base refinement using apprenticeship learning techniques

    • David C. Wilkins

    Pages 247-257

15. Creating high level knowledge structures from simple elements

    • Michael Pazzani

    Pages 258-288

16. Demand-driven concept formation

    • Stefan Wrobel

    Pages 289-319

Machine learning has become a rapidly growing field of Artificial Intelligence. Since the First International Workshop on Machine Learning in 1980, the number of scientists working in the field has been increasing steadily. This situation allows for specialization within the field. There are two types of specialization: on subfields or, orthogonal to them, on special subjects of interest. This book follows the thematic orientation. It contains research papers, each of which throws light upon the relation between knowledge representation, knowledge acquisition and machine learning from a different angle. Building up appropriate representations is considered to be the main concern of knowledge acquisition for knowledge-based systems throughout the book. Here machine learning is presented as a tool for building up such representations. But machine learning itself also states new representational problems. This book gives an easy-to-understand insight into a new field with its problems and the solutions it offers. Thus it will be of good use to both experts and newcomers to the subject.

• artificial intelligence
• expert system
• inference engine
• intelligence
• knowledge base
• knowledge representation
• knowledge-based system
• knowledge-based systems
• learning
• machine learning
• ontology