Learning Context-Free Grammars with a Simplicity Bias
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Programming by Demonstration Using Version Space Algebra
Machine Learning
Version spaces and the consistency problem
Artificial Intelligence
LARS: A learning algorithm for rewriting systems
Machine Learning
Identifying hierarchical structure in sequences: a linear-time algorithm
Journal of Artificial Intelligence Research
A bibliographical study of grammatical inference
Pattern Recognition
Version spaces without boundary sets
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Generating multimodal grammars for multimodal dialogue processing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In principle, the version space approach can be applied to any induction problem. However, in some cases the representation language for generalizations is so powerful that (1) some of the update functions for the version space are not effectively computable, and (2) the version space contains infinitely many generalizations. The class of context-free grammars is a simple representation that exhibits these problems. This paper presents an algorithm that solves both problems for this domain. Given a sequence of strings, the algorithm incrementally constructs a data structure that has nearly all the beneficial properties of a version space. The algorithm is fast enough to solve small induction problems completely, and it serves as a framework for biases that permit the solution of larger problems heuristically. The same basic approach may be applied to representations that include context-free grammars as special cases, such as And-Or graphs, production systems, and Horn clauses.