Machine learning of inductive bias
Machine learning of inductive bias
The role of explicit contextual knowledge in learning concepts to improve performance
The role of explicit contextual knowledge in learning concepts to improve performance
Explanation-Based Generalization: A Unifying View
Machine Learning
Learning at the Knowledge Level
Machine Learning
Mechanical transformation of task heuristics into operational procedures
Mechanical transformation of task heuristics into operational procedures
Selectively generalizing plans for problem-solving
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Learning schemata for natural language processing
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
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Operationality is the key property that distinguishes the final description learned in an explanation-based 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. 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.