Knowledge Acquisition: Selected Research and Commentary

1. Front Matter

   Pages i-iii

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2. Introduction: A Sampler in Knowledge Acquisition for the Machine Learning Community

   • Sandra Marcus

   Pages 1-3

3. Can Machine Learning Offer Anything to Expert Systems?

   • Bruce G. Buchanan

   Pages 5-8

4. When Will Machines Learn?

   • Douglas B. Lenat

   Pages 9-11

5. Supporting Start-to-Finish Development of Knowledge Bases

   • Ray Bareiss, Bruce W. Porter, Kenneth S. Murray

   Pages 13-37

6. The Knowledge Level Reinterpreted: Modeling How Systems Interact

   • William J. Clancey

   Pages 39-45

7. Automated Knowledge Acquisition for Strategic Knowledge

   • Thomas R. Gruber

   Pages 47-90

8. The World Would Be a Better Place if Non-Programmers Could Program

   • John McDermott

   Pages 91-92

9. Task-Structures, Knowledge Acquisition and Learning

   • B. Chandrasekaran

   Pages 93-99

10. Automated Support for Building and Extending Expert Models

    • Mark A. Musen

    Pages 101-129

11. Knowledge Acquisition for Knowledge-Based Systems: Notes on the State-of-the-Art

    • John H. Boose, Brian R. Gaines

    Pages 131-148

12. Back Matter

    Pages 149-152

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What follows is a sampler of work in knowledge acquisition. It comprises three technical papers and six guest editorials. The technical papers give an in-depth look at some of the important issues and current approaches in knowledge acquisition. The editorials were pro­ duced by authors who were basically invited to sound off. I've tried to group and order the contributions somewhat coherently. The following annotations emphasize the connections among the separate pieces. Buchanan's editorial starts on the theme of "Can machine learning offer anything to expert systems?" He emphasizes the practical goals of knowledge acquisition and the challenge of aiming for them. Lenat's editorial briefly describes experience in the development of CYC that straddles both fields. He outlines a two-phase development that relies on an engineering approach early on and aims for a crossover to more automated techniques as the size of the knowledge base increases. Bareiss, Porter, and Murray give the first technical paper. It comes from a laboratory of machine learning researchers who have taken an interest in supporting the development of knowledge bases, with an emphasis on how development changes with the growth of the knowledge base. The paper describes two systems. The first, Protos, adjusts the training it expects and the assistance it provides as its knowledge grows. The second, KI, is a system that helps integrate knowledge into an already very large knowledge base.

• artificial intelligence
• expert system
• knowledge
• knowledge base
• knowledge-based system
• knowledge-based systems
• learning
• machine learning
• modeling

• Boeing Computer Services, Seattle, USA

  Sandra Marcus

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