Operationality is the key property that distinguishes the final description learned in an explanationbased system from the initial concept description input to the system. Yet most existing systems fail to define operationality with necessary precision. In particular, attempts to define operationality in terms of "efficient instance recognition" tacitly incorporate several unrealistic, simplifying assumptions about the learner's performance task and the type of performance improvement desired. Over time, these assumptions are likely to be violated, and the learning system's effectiveness will deteriorate. In this paper, we survey how operationality is defined and assessed in several explanation-based systems, and then present a more comprehensive definition of operationality. We also describe an implemented system that incorporates our new definition and overcomes some of the limitations exhibited by current operationality assessment schemes.
* This is a revised version of a paper that was recognized as the most outstanding paper in the subfield "Machine Learning" at AAAI-87. The work was done at the Department of Computer Science, Rutgers University, New Brunswick, N J, and was published in a somewhat shorter form as [9].
1 The target concept is referred to as the goal concept in [15].