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Disibutional learning of some contextfree languages with a minimally adequate teacher
 In Proceedings of the International Conference on Grammatical Inference, ICGI’10
"... Abstract. Angluin showed that the class of regular languages could be learned from a Minimally Adequate Teacher (mat) providing membership and equivalence queries. Clark and Eyraud (2007) showed that some context free grammars can be identified in the limit from positive data alone by identifying t ..."
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Abstract. Angluin showed that the class of regular languages could be learned from a Minimally Adequate Teacher (mat) providing membership and equivalence queries. Clark and Eyraud (2007) showed that some context free grammars can be identified in the limit from positive data alone by identifying the congruence classes of the language. In this paper we consider learnability of context free languages using a mat. We show that there is a natural class of context free languages, that includes the class of regular languages, that can be polynomially learned from a mat, using an algorithm that is an extension of Angluin’s lstar algorithm. 1
A framework for the competitive evaluation of model inference techniques
 IN FIRST INTERNATIONAL WORKSHOP ON MODEL INFERENCE IN TESTING
, 2010
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Learning and testing the bounded retransmission protocol
 University of Maryland, College Park, USA
"... Abstract Using a wellknown industrial case study from the verification literature, the bounded retransmission protocol, we show how active learning can be used to establish the correctness of protocol implementation I relative to a given reference implementation R. Using active learning, we learn ..."
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Abstract Using a wellknown industrial case study from the verification literature, the bounded retransmission protocol, we show how active learning can be used to establish the correctness of protocol implementation I relative to a given reference implementation R. Using active learning, we learn a model M R of reference implementation R, which serves as input for a model based testing tool that checks conformance of implementation I to M R . In addition, we also explore an alternative approach in which we learn a model M I of implementation I, which is compared to model M R using an equivalence checker. Our work uses a unique combination of software tools for model construction (Uppaal), active learning (LearnLib, Tomte), modelbased testing (JTorX, TorXakis) and verification (CADP, MRMC). We show how these tools can be used for learning these models, analyzing the obtained results, and improving the learning performance.
Implementing KearnsVazirani Algorithm for Learning DFA Only with Membership Queries
"... Abstract. Two algorithms for learning dfa with membership queries are described. Both of them are based on Kearns and Vazirani’s version of Angluin’s L ∗. Our algorithms tied in the third place in the Zulu competition. 1 ..."
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Abstract. Two algorithms for learning dfa with membership queries are described. Both of them are based on Kearns and Vazirani’s version of Angluin’s L ∗. Our algorithms tied in the third place in the Zulu competition. 1
Results of the Sequence PredIction ChallengE (SPiCe): a Competition on Learning the Next Symbol in a Sequence
, 2016
"... Abstract The Sequence PredIction ChallengE (SPiCe) is an online competition that took place between March and July 2016. Each of the 15 problems was made of a set of whole sequences as training sample, a validation set of prefixes, and a test set of prefixes. The aim was to submit a ranking of the ..."
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Abstract The Sequence PredIction ChallengE (SPiCe) is an online competition that took place between March and July 2016. Each of the 15 problems was made of a set of whole sequences as training sample, a validation set of prefixes, and a test set of prefixes. The aim was to submit a ranking of the 5 most probable symbols to be the next symbol of each prefix.