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A Knowledge-Level Model of a Configurable Learning System
August 1996 (vol. 11 no. 4)
pp. 50-58

This proposed learning system model lets engineers experiment more easily with alternative learning-tool configurations when developing knowledge-based applications.

THE GROWING COMPLEXITY OF KNOWLEDGE-BASED applications makes it necessary to use automated or semiautomated techniques during knowledge acquisition. Knowledge engineering can also involve using various machine-learning systems, accepting different concept description languages or working with a variety of learning techniques that manage varied inputs such as example sets or background knowledge.

For a specific task, knowledge engineers thus might need to experiment with different learning algorithms and their variations. In this context, they must

  • 1. select a machine-learning tool suited to the task,
  • 2. set the parameters that control the tool's behavior, and
  • 3. run the tool, analyze the results, and stop or return to steps 1 or 2.
  • Running this loop might often be difficult and tedious. Selecting the appropriate tool might take time, and finding parameter settings well-suited to the problem's characteristics might also be time-consuming and difficult. Systems that provide a family of learning algorithms in a unified, rather than a single environment, can lessen this loop's complexity and duration.

    Céline Rouveirol, Patrick Albert, "A Knowledge-Level Model of a Configurable Learning System," IEEE Intelligent Systems, vol. 11, no. 4, pp. 50-58, Aug. 1996, doi:10.1109/64.511867
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