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Title: Learning perceptual schemas to avoid the utility problem
Authors: Lane, PCR
Cheng, PCH
Gobet, F
Keywords: CHREST
utility problem
machine learning
human learning
perceptual expertise
Electric Circuits
Diagrammatic Representations
algebraic Representations
perceptual schema
multiple representations
Publication Date: 1999
Publisher: Springer
Citation: Proceedings of the Nineteenth SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence, Cambridge, 1999, pp. 72-82
Abstract: This paper describes principles for representing and organising planning knowledge in a machine learning architecture. One of the difficulties with learning about tasks requiring planning is the utility problem: as more knowledge is acquired by the learner, the utilisation of that knowledge takes on a complexity which overwhelms the mechanisms of the original task. This problem does not, however, occur with human learners: on the contrary, it is usually the case that, the more knowledgeable the learner, the greater the efficiency and accuracy in locating a solution. The reason for this lies in the types of knowledge acquired by the human learner and its organisation. We describe the basic representations which underlie the superior abilities of human experts, and describe algorithms for using equivalent representations in a machine learning architecture.
Appears in Collections:Psychology
Dept of Life Sciences Research Papers

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