Making believers out of computers
Artificial Intelligence
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Using distribution-free learning theory to analyze chunking
Proceedings of the eighth biennial conference of the Canadian Society for Computational Studies of Intelligence on CSCSI-90
Finding optimal derivation in redundant knowledge bases
Artificial Intelligence
Hierarchical knowledge bases and efficient disjunctive reasoning
Proceedings of the first international conference on Principles of knowledge representation and reasoning
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Learning at the Knowledge Level
Machine Learning
A theory of unsupervised speedup learning
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
A statistical approach to solving the EBL utility problem
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Intermediate depth representations
Artificial Intelligence in Medicine
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This report discusses what it means to claim that a representation is an effective encoding of knowledge. We first present dimensions of merit for evaluating representations, based on the view that usefulness is a behavioral property, and is necessarily relative to a specified task. We then provide methods (based on results from mathematical statistics) for reliably measuring effectiveness empirically, and hence for comparing different representations. We also discuss weak but guaranteed methods of improving inadequate representations. Our results are an application of the ideas of formal learning theory to concrete knowledge representation formalisms.