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Learning models defining recursive computations, like autom ata and formal grammars, are the core of the field called Grammatical Inference (GI). The expressive power of these models and the complexity of the a ssociated computational problems are major researc h topics within mathematical logic and computer sc ience. Historically, there has been little interac tion between the GI and ICALP communities, though recently some important results started to bridge the gap between both worlds, including application s of learning to formal verification and model che cking, and (co-)algebraic formulations of automata and grammar learning algorithms.

\nThe ai m of this workshop is to bring together experts on logic who could benefit from grammatical inferenc e tools, and researchers in grammatical inference who could find in logic and verification new fruit ful applications for their methods. The LearnAut w orkshop will consists of a number of invited talks , other talks from researchers who submitted their work to the workshop, and discussions. An importa nt amount of time will be kept for interactions be tween participants.

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URL:https://learnaut24.github.io/
CONTACT:Matteo Sammartino at matteo.sammartino at
rhul.ac.uk
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We invite submissions of recent work, including preliminary research, related to the theme of the workshop. T he Program Committee will select a subset of the a bstracts for oral presentation. At least one autho r of each accepted abstract is expected to represe nt it at the workshop. Note that accepted papers w ill be made available on the workshop website but will not be part of formal proceedings (i.e., Lear nAut is a non-archival workshop).