Learning regular sets from queries and counterexamples
Information and Computation
Learning context-free grammars from structural data in polynomial time
Theoretical Computer Science
Learning Regular Languages Using RFSA
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Residual Finite State Automata
Fundamenta Informaticae
Learning tree languages from positive examples and membership queries
Theoretical Computer Science
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
DLT'03 Proceedings of the 7th international conference on Developments in language theory
Learning a regular tree language from a teacher
DLT'03 Proceedings of the 7th international conference on Developments in language theory
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The area of Grammatical Inference centers on learning algorithms: Algorithms that infer a description (e.g., a grammar or an automaton) for an unknown formal language from given information in finitely many steps. Various conceivable learning settings have been outlined, and based on those a range of algorithms have been developed. One of the language classes studied most extensively with respect to its algorithmical learnability is the class of regular string languages. Possible sources of information include membership queries (MQs) where a learner may query an oracle if a certain element is in the target language L, and equivalence queries (EQs) where a learner may ask if the current hypothesis is correct and is given a counterexample if this is not the case. Moreover, a learner can for example be presented with a positive sample, i.e., a finite subset of L.