Learning regular sets from queries and counterexamples
Information and Computation
Negative Results for Equivalence Queries
Machine Learning
Efficient Algorithms for the Inference of Minimum Size DFAs
Machine Learning
Learning Minimal Separating DFA's for Compositional Verification
TACAS '09 Proceedings of the 15th International Conference on Tools and Algorithms for the Construction and Analysis of Systems: Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2009,
On the Synthesis of Finite-State Machines from Samples of Their Behavior
IEEE Transactions on Computers
FMCO'06 Proceedings of the 5th international conference on Formal methods for components and objects
Exact DFA identification using SAT solvers
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Inferring network invariants automatically
IJCAR'06 Proceedings of the Third international joint conference on Automated Reasoning
Learning techniques for software verification and validation
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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A prominent learning algorithm is Angluin's L∗ algorithm, which allows to learn a minimal deterministic automaton using so-called membership and equivalence queries addressed to a teacher. In many applications, however, a teacher might be unable to answer some of the membership queries because parts of the object to learn are not completely specified, not observable, it is too expensive to resolve these queries, etc. Then, these queries may be answered inconclusively. In this paper, we survey different algorithms to learn minimal deterministic automata in this setting in a coherent fashion. Moreover, we provide modifications and improvements for these algorithms, which are enabled by recent developments.