4 July 2022, LearnAut 2022: Learning & Automata, Virtual and Paris, France
Learning models defining recursive computations, like automata and formal grammars, are the core of the field called Grammatical Inference (GI). The expressive power of these models and the complexity of the associated computational problems are major research topics within mathematical logic and computer science. Historically, there has been little interaction between the GI and ICALP communities, though recently some important results started to bridge the gap between both worlds, including applications of learning to formal verification and model checking, and (co-)algebraic formulations of automata and grammar learning algorithms. The goal of this workshop is to bring together experts on logic who could benefit from grammatical inference tools, and researchers in grammatical inference who could find in logic and verification new fruitful applications for their methods.
We invite submissions of recent work, including preliminary research, related to the theme of the workshop. The Program Committee will select a subset of the abstracts for oral presentation. At least one author of each accepted abstract is expected to represent it at the workshop (in person, or virtually). Note that accepted papers will be made available on the workshop website but will not be part of formal proceedings (i.e., LearnAut is a non-archival workshop). Submissions in the form of extended abstracts must be at most 8 single-column pages long at most (plus at most four for bibliography and possible appendixes) and must be submitted in the JMLR/PMLR format. We do accept submissions of work recently published or currently under review.