Abstract

View of Learning To implement a program that somehow "learns" it is neccessary to fix a set of concepts to be learned and develop a representation for the concepts and examples of the concepts. In order to investigate general properties of machine learning it is neccesary to work in as representation independent fashion as possible. In this work, we consider machines that learn programs for recursive functions. Several authors have argued that such studies are general enough to include a wide array of learning situations [2,3,22,23,24]. For example, a behavior to be learned can be modeled as a set of stimulus and response pairs. Assuming that any behavior associates only one response to each possible stimulus, behaviors can be viewed as functions from stimuli to responses. Some behaviors, such as anger, are not easily modeled as functions. Our primary interest, however, concerns the learning of fundamental behaviors such as reading (mapping symbols to sounds), recognition (mapping pa...

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