Learning regular sets from queries and counterexamples
Information and Computation
Efficient learning of context-free grammars from positive structural examples
Information and Computation
Attribute grammar paradigms—a high-level methodology in language implementation
ACM Computing Surveys (CSUR)
On evolutionary exploration and exploitation
Fundamenta Informaticae
The use of grammatical inference for designing programming languages
Communications of the ACM
Learning DFA from Simple Examples
Machine Learning
Learning Context-Free Grammars with a Simplicity Bias
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Synthesizing Context Free Grammars from Sample Strings Based on Inductive CYK Algorithm
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Learning Context-Free Grammars from Partially Structured Examples
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Extracting grammar from programs: brute force approach
ACM SIGPLAN Notices
Grammatical Inference in Bioinformatics
IEEE Transactions on Pattern Analysis and Machine Intelligence
When and how to develop domain-specific languages
ACM Computing Surveys (CSUR)
Compilers: Principles, Techniques, and Tools (2nd Edition)
Compilers: Principles, Techniques, and Tools (2nd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Genetic Programming with Incremental Learning for Grammatical Inference
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Algorithms on Strings
Towards Machine Learning of Grammars and Compilers of Programming Languages
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
State-Merging DFA Induction Algorithms with Mandatory Merge Constraints
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
AN UNSUPERVISED INCREMENTAL LEARNING ALGORITHM FOR DOMAIN-SPECIFIC LANGUAGE DEVELOPMENT
Applied Artificial Intelligence
Hybrid Evolutionary Algorithms
Hybrid Evolutionary Algorithms
Identifying hierarchical structure in sequences: a linear-time algorithm
Journal of Artificial Intelligence Research
Learning context-free grammar using improved tabular representation
Applied Soft Computing
To explore or to exploit: An entropy-driven approach for evolutionary algorithms
International Journal of Knowledge-based and Intelligent Engineering Systems
A bibliographical study of grammatical inference
Pattern Recognition
Learning context-free grammars using tabular representations
Pattern Recognition
Evolutionary induction of stochastic context free grammars
Pattern Recognition
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
A note on teaching-learning-based optimization algorithm
Information Sciences: an International Journal
Hi-index | 0.00 |
An unsupervised incremental algorithm for grammar inference and its application to domain-specific language development are described. Grammatical inference is the process of learning a grammar from the set of positive and optionally negative sentences. Learning general context-free grammars is still considered a hard problem in machine learning and is not completely solved yet. The main contribution of the paper is a newly developed memetic algorithm, which is a population-based evolutionary algorithm enhanced with local search and a generalization process. The learning process is incremental since a new grammar is obtained from the current grammar and false negative samples, which are not parsed by the current grammar. Despite being incremental, the learning process is not sensitive to the order of samples. All important parts of this algorithm are explained and discussed. Finally, a case study of a domain specific language for rendering graphical objects is used to show the applicability of this approach.