Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Effective and Efficient Knowledge Base Refinement
Machine Learning
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using genetic programming to learn and improve control knowledge
Artificial Intelligence
Explanation-Based Generalization: A Unifying View
Machine Learning
Simplifying decision trees: A survey
The Knowledge Engineering Review
An approach to fuzzy multiattribute decision making under uncertainty
Information Sciences: an International Journal
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Linear programming method for multiattribute group decision making using IF sets
Information Sciences: an International Journal
Learning Linear Ranking Functions for Beam Search with Application to Planning
The Journal of Machine Learning Research
Changing the rules: a comprehensive approach to theory refinement
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Rival penalized competitive learning for clustering analysis, RBF net, and curve detection
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Abstract: Humans frequently have to face complex problems. A classical approach to solve them is to search the solution by means of a trial and error method. This approach is often used with success by artificial systems. However, when facing highly complex problems, it becomes necessary to introduce control knowledge (heuristics) in order to limit the number of trials needed to find the optimal solution. Unfortunately, acquiring and maintaining such knowledge can be fastidious. In this paper, we propose an automatic knowledge revision approach for systems based on a trial and error method. Our approach allows to revise the knowledge off-line by means of experiments. It is based on the analysis of solved instances of the considered problem and on the exploration of the knowledge space. Indeed, we formulate the revision problem as a search problem: we search the knowledge set that maximises the performances of the system on a sample of problem instances. Our knowledge revision approach has been implemented for a real-world industrial application: automated cartographic generalisation, a complex task of the cartography domain. In this implementation, we demonstrate that our approach improves the quality of the knowledge and thus the performance of the system.