An analytical comparison of some rule-learning programs
Artificial Intelligence
The network control assistant (NCA), a real-time prototype expert system for network management
IEA/AIE '88 Proceedings of the 1st international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1
Conflict resolution strategies for nonhierarchical distributed agents
Distributed artificial intelligence: vol. 2
ILS: a framework for multi-paradigmatic learning
Proceedings of the seventh international conference (1990) on Machine learning
Unifying themes in empirical and explanation-based learning
Proceedings of the sixth international workshop on Machine learning
A framework for integrating heterogeneous learning agents
Second generation expert systems
A Heuristic Approach to the Discovery of Macro-Operators
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
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One of the goals of Machine Learning is the production of software that can improve itself. Such software can learn from experience and adapt to changing situations and requirements. In addition, such software can refine its knowledge-base, perhaps leading to a level of expertise beyond that of human experts.This paper describes NETMAN, a knowledge-based program that uses a machine learning technique, Knowledge-based Learning, in the domain of Network Traffic Control. NETMAN's task is to maximize call completion in a circuit-switched telecommunications network. NETMAN learns from its own experiences and by observing the actions of other agents.NETMAN is one of the components of ILS (Integrated Learning System), which contains implementations of several learning paradigms working together to improve problem-solving performance. NETMAN combines two machine learning paradigms: Explanation-Based Learning and Empirical Learning.