Conference proceeding
Application of machine learning to the maintenance of knowledge-based performance
Proceedings of the 3rd international conference on industrial and engineering applications of artificial intelligence and expert systems, Vol.2, pp.1005-1012
IEA/AIE '90
06/01/1990
Handle:
https://hdl.handle.net/2376/107795
Abstract
Integration of machine learning methods into knowledge-based systems requires greater control over the application of the learning methods. Recent research in machine learning has shown that isolated and unconstrained application of learning methods can eventually degrade performance. This paper presents an approach called performance-driven knowledge transformation for controlling the application of learning methods. The primary guidance for the control is performance of the knowledge base. The approach is implemented in the PEAK system. Two experiments with PEAK illustrate how the knowledge base is transformed using different learning methods to maintain performance goals. Results demonstrate the ability of performance-driven knowledge transformation to control the application of learning methods and maintain knowledge base performance.
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Details
- Title
- Application of machine learning to the maintenance of knowledge-based performance
- Creators
- Lawrence Holder
- Publication Details
- Proceedings of the 3rd international conference on industrial and engineering applications of artificial intelligence and expert systems, Vol.2, pp.1005-1012
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Series
- IEA/AIE '90
- Publisher
- ACM
- Identifiers
- 99900547345301842
- Language
- English
- Resource Type
- Conference proceeding