Handbook of theoretical computer science (vol. B)
Learning Automata from Ordered Examples
Machine Learning - Connectionist approaches to language learning
Random DFA's can be approximately learned from sparse uniform examples
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Handbook of logic in computer science (vol. 1)
What Is the Search Space of the Regular Inference?
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
A Polynominal Time Incremental Algorithm for Learning DFA
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
FORTE XII / PSTV XIX '99 Proceedings of the IFIP TC6 WG6.1 Joint International Conference on Formal Description Techniques for Distributed Systems and Communication Protocols (FORTE XII) and Protocol Specification, Testing and Verification (PSTV XIX)
Automated black-box testing of functional correctness using function approximation
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Dynamic testing via automata learning
HVC'07 Proceedings of the 3rd international Haifa verification conference on Hardware and software: verification and testing
Incremental learning-based testing for reactive systems
TAP'11 Proceedings of the 5th international conference on Tests and proofs
Learning-based testing for reactive systems using term rewriting technology
ICTSS'11 Proceedings of the 23rd IFIP WG 6.1 international conference on Testing software and systems
ISoLA'12 Proceedings of the 5th international conference on Leveraging Applications of Formal Methods, Verification and Validation: technologies for mastering change - Volume Part I
An incremental learning algorithm for extended mealy automata
ISoLA'12 Proceedings of the 5th international conference on Leveraging Applications of Formal Methods, Verification and Validation: technologies for mastering change - Volume Part I
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We introduce a new algorithm for sequential learning of Mealy automata by congruence generator extension (CGE). Our approach makes use of techniques from term rewriting theory and universal algebra for compactly representing and manipulating automata using finite congruence generator sets represented as string rewriting systems (SRS). We prove that the CGE algorithm correctly learns in the limit.