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Rational Learning: Finding A Balance between Utility and Efficiency.

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Bibliographic Details
Title: Rational Learning: Finding A Balance between Utility and Efficiency.
Language: English
Authors: Gratch, Jonathan, Illinois Univ., Urbana. Dept. of Computer Science.
Peer Reviewed: N
Page Count: 20
Publication Date: 1992
Sponsoring Agency: National Science Foundation, Washington, DC.
Document Type: Reports - Descriptive
Descriptors: Computer Assisted Instruction, Computer Uses in Education, Costs, Efficiency, Learning Strategies, Technological Advancement
Abstract: The field of machine learning has developed a wide array of techniques for improving the effectiveness of performance elements. Ideally, a learning system would adapt its commitments to the demands of a particular learning situation, rather than relying on fixed commitments that impose tradeoffs between the efficiency and utility of a learning technique. This article presents an extension of the COMPOSER learning approach that dynamically adjusts its learning behavior based on the resources available for learning. COMPOSER is a speed-up learning technique that provides a statistical approach to the utility problem. The system identifies a sequence of transformations that, with high probability, increase the Type I utility of an initial planning system. The approach breaks the task into a learning phase and a utilization phase. This extension to COMPOSER adopts a rational policy that dynamically balances the trade-off between efficiency and utility. Implications for learning systems are discussed. (Contains 24 references.) (SLD)
Entry Date: 1994
Accession Number: ED367700
Database: ERIC
Description
Abstract:The field of machine learning has developed a wide array of techniques for improving the effectiveness of performance elements. Ideally, a learning system would adapt its commitments to the demands of a particular learning situation, rather than relying on fixed commitments that impose tradeoffs between the efficiency and utility of a learning technique. This article presents an extension of the COMPOSER learning approach that dynamically adjusts its learning behavior based on the resources available for learning. COMPOSER is a speed-up learning technique that provides a statistical approach to the utility problem. The system identifies a sequence of transformations that, with high probability, increase the Type I utility of an initial planning system. The approach breaks the task into a learning phase and a utilization phase. This extension to COMPOSER adopts a rational policy that dynamically balances the trade-off between efficiency and utility. Implications for learning systems are discussed. (Contains 24 references.) (SLD)