On the learnability of infinitary regular sets
Information and Computation
Exact learning Boolean functions via the monotone theory
Information and Computation
LearnLib: a framework for extrapolating behavioral models
International Journal on Software Tools for Technology Transfer (STTT) - Special Section on FMICS 05
Regular Model Checking Using Inference of Regular Languages
Electronic Notes in Theoretical Computer Science (ENTCS)
Learning assumptions for compositional verification
TACAS'03 Proceedings of the 9th international conference on Tools and algorithms for the construction and analysis of systems
TACAS'11/ETAPS'11 Proceedings of the 17th international conference on Tools and algorithms for the construction and analysis of systems: part of the joint European conferences on theory and practice of software
Deriving invariants by algorithmic learning, decision procedures, and predicate abstraction
VMCAI'10 Proceedings of the 11th international conference on Verification, Model Checking, and Abstract Interpretation
libalf: the automata learning framework
CAV'10 Proceedings of the 22nd international conference on Computer Aided Verification
Automated assume-guarantee reasoning through implicit learning
CAV'10 Proceedings of the 22nd international conference on Computer Aided Verification
Demonstrating learning of register automata
TACAS'12 Proceedings of the 18th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Learning boolean functions incrementally
CAV'12 Proceedings of the 24th international conference on Computer Aided Verification
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We present the tool BULL (Boolean fUnction Learning Library), the first publicly available implementation of learning algorithms for Boolean functions. The tool is implemented in C with interfaces to C++, JAVA and OCAML. Experimental results show significant advantages of Boolean function learning algorithms over all variants of the L* learning algorithm for regular languages.