An analytical comparison of some rule-learning programs
Artificial Intelligence
Machine learning: a guide to current research
Machine learning: a guide to current research
Production system models of learning and development
Production system models of learning and development
Artificial intelligence (2nd ed.)
Artificial intelligence (2nd ed.)
Knowledge acquisition by incremental learning from problem-solution pairs
Computational Intelligence
Empirical Analysis for Expert Systems
Empirical Analysis for Expert Systems
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
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LEW (learning by watching), a machine learning system, is described. It was designed for knowledge acquisition in cooperation with an expert. LEW learns from examples of problem-solution (or question-answer) pairs by generalizing on differences in those pairs. In this sense, it belongs to the family of inductive learning methods. It provides for using background knowledge through the environment component of problem-solution pairs, thereby making constructive learning possible. The user can control the extent of the generalizations performed by LEW. The learning method is incremental and, to some extent, noise-resistant. The authors give an informal overview of the knowledge representation and the basic learning algorithm of LEW and indicate that the system's design meets the stated criteria and enables it to give helpful assistance, even in situations characterized by noisy or conflicting information and by lack of extensive background knowledge. The theory behind LEW is presented, along with rigorous definitions of its fundamental concepts and a general description of its learning algorithm. LEW's functioning with some larger examples, one from the QUIZ Advisor domain and another from the domain of block-world planning, is illustrated. The authors compare LEW with several other knowledge acquisition tools and introduce a precise characterization of learning from near misses and a near-miss metric. Possible extensions and enhancements to the system are noted.