| 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 |