Active learning for extended finite state machines

DOI10.1007/s00165-016-0355-5zbMath1342.68174OpenAlexW2213779776MaRDI QIDQ736459

Bengt Jonsson, Bernhard Steffen, Sofia Cassel, Falk Howar

Publication date: 4 August 2016

Published in: Formal Aspects of Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00165-016-0355-5

Mathematics Subject Classification ID

Computational learning theory (68Q32) Formal languages and automata (68Q45)

Related Items (15)

Learning One-Clock Timed Automata ⋮ Inferring regular languages and \(\omega\)-languages ⋮ Model checking, synthesis, and learning ⋮ Never-stop context-free learning ⋮ A taxonomy and reductions for common register automata formalisms ⋮ Active learning for deterministic bottom-up nominal tree automata ⋮ Enhancing active model learning with equivalence checking using simulation relations ⋮ A Myhill-Nerode theorem for finite state matrix automata and finite matrix languages ⋮ Polynomial-time equivalence testing for deterministic fresh-register automata ⋮ Learning Symbolic Automata ⋮ \(L^\ast\)-based learning of Markov decision processes (extended version) ⋮ Fast computations on ordered nominal sets ⋮ Active learning for extended finite state machines ⋮ A Myhill-Nerode theorem for register automata and symbolic trace languages ⋮ Efficient active automata learning via mutation testing

Uses Software

• Daikon
• z3
• Reo
• LearnLib
• RALib
• Sigma*

Cites Work

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• Learning register automata: from languages to program structures
• Improving active Mealy machine learning for protocol conformance testing
• Active learning for extended finite state machines
• Learning regular sets from queries and counterexamples
• Generating models of infinite-state communication protocols using regular inference with abstraction
• Inference of finite automata using homing sequences
• The Daikon system for dynamic detection of likely invariants
• Model-based testing of reactive systems. Advanced lectures.
• Inferring Canonical Register Automata
• Sigma*
• Automata Learning with Automated Alphabet Abstraction Refinement
• Automata Learning through Counterexample Guided Abstraction Refinement
• Reo: a channel-based coordination model for component composition
• Mining specifications
• Software model checking
• Synthesis of interface specifications for Java classes
• A Fresh Approach to Learning Register Automata
• Computer Aided Verification

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