LearnAut 2019

Call for papers

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, spanning the communities that the Logic in Computer Science (LICS) conference brings together. Historically, there has been little interaction between the GI and LICS 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. Similarly to how main machine learning conferences and workshops are organized, all accepted abstracts will be part of a poster session held during the workshop. Additionally, 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. Note that participation to the poster session is on a voluntary basis for papers selected for oral presentation. High-quality submissions will be strongly encouraged to submit an extended version to an upcoming special issue of the Machine Learning Journal.