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

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.