Initiality, induction, and computability
Algebraic methods in semantics
Learning regular sets from queries and counterexamples
Information and Computation
Handbook of theoretical computer science (vol. B)
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)
Handbook of logic in computer science (vol. 2)
Foundations of Logic Programming
Foundations of Logic Programming
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
Grammatical Inference: Learning Automata and Grammars
Grammatical Inference: Learning Automata and Grammars
CGE: a sequential learning algorithm for mealy automata
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
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
Model-Based testing and model inference
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 present a new algorithm ICGE for incremental learning of extended Mealy automata computing over abstract data types. Our approach extends and refines our previous research on congruence generator extension (CGE) as an algebraic approach to automaton learning. In the congruence generator approach, confluent terminating string rewriting systems (SRS) are used to represent hypothesis automata. We show how an approximating sequence R0 , R1 , … of confluent terminating SRS can be directly and incrementally generated from observations about the loop structure of an unknown automaton A. Such an approximating sequence converges finitely if A is finite state, and converges in the limit if A is an infinite state automaton.