Communications of the ACM
Learning regular sets from queries and counterexamples
Information and Computation
Polynomial Time Learnability of Simple Deterministic Languages
Machine Learning
Learning context-free grammars from structural data in polynomial time
Theoretical Computer Science
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
A hierarchy of language families learnable by regular language learning
Information and Computation
Algorithmic Program DeBugging
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Introduction to Formal Language Theory
Introduction to Formal Language Theory
Machine Learning
Machine Learning
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Language Learning from Membership Queries and Characteristic Examples
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Polynomial Time Probabilistic Learning of a Subclass of Linear Languages with Queries
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
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We show that simple deterministic languages are polynomial time learnable via membership queries if the learner knows a special finite set of positive examples. This finite set is called a representative sample and has been introduced by Angluin Inform. Control 51 (1981) to show that regular languages are polynomial time learnable via membership queries. If simple deterministic languages are learnable in polynomial time via membership and equivalence queries, we can obtain a representative sample of a target language in polynomial time from a correct hypothesis. Thus, our result implies that the polynomial time learning problem of simple deterministic languages via membership and equivalence queries is solvable if and only if we can find a representative sample in polynomial time via these queries. We show the learnability of simple deterministic languages by giving a learning algorithm. The algorithm, at the first stage, makes all possible candidate rules to generate the target language and a set of simple deterministic grammars which are little different each other. Then, comparing them, the algorithm eliminates inappropriate rules.