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Richard M. Keller

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AIJ Journal 1988 Journal Article

Defining operationality for explanation-based learning

Richard M. Keller

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. 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.

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AAAI Conference 1987 Conference Paper

Defining Operationality for Explanation-Based Learning

Richard M. Keller

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.

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AAAI Conference 1983 Conference Paper

Learning by Re-Expressing Concepts for Efficient Recognition

Richard M. Keller

Much attention in the field of machine learning has been directed at the problem of inferring concept descriptions from examples. But in many learning situations, we are initrally presented with a fully-formed concept description, and our goal IS instead to re-express that description with some particular task in mind. In this paper, we specifjcally consider the task of recognizing concept instances efficiently. We describe how concepts that are accurate, though computationally inefficient for use in recognizing instances, can be re-expressed in an efficient form through a process we call concept operationalization Various techniques for concept operationalization are illustrated in the context of the LEX learning system.

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