Rational Learning: Finding A Balance between Utility and Efficiency.
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| 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 |
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