BEGIN:VCALENDAR VERSION:2.0 PRODID:ILLC Website X-WR-TIMEZONE:Europe/Amsterdam BEGIN:VTIMEZONE TZID:Europe/Amsterdam X-LIC-LOCATION:Europe/Amsterdam BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:19700329T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:19701025T030000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:/NewsandEvents/Archives/2022/newsitem/13415/4- July-2022-4th-workshop-on-Learning-Automata-LearnA ut-2022-Virtual-and-Paris-France DTSTAMP:20220630T153452 SUMMARY:4th workshop on Learning & Automata (Learn Aut 2022), Virtual and Paris, France DTSTART;VALUE=DATE:20220704 DTEND;VALUE=DATE:20220704 LOCATION:Virtual and Paris, France DESCRIPTION:Learning models defining recursive com putations, like automata and formal grammars, are the core of the field called Grammatical Inference (GI). The expressive power of these models and th e complexity of the associated computational probl ems are major research topics within mathematical logic and computer science. Historically, there ha s been little interaction between the GI and ICALP communities, though recently some important resul ts started to bridge the gap between both worlds, including applications of learning to formal verif ication and model checking, and (co-)algebraic for mulations of automata and grammar learning algorit hms. The goal of this workshop is to bring togeth er experts on logic who could benefit from grammat ical inference tools, and researchers in grammatic al inference who could find in logic and verificat ion new fruitful applications for their methods. T he LearnAut workshop will consist of 3 invited tal ks and 14 contributed talks from researchers whose submitted works were selected after a double-blin d peer-reviewed phase. A significant amount of tim e will be kept for interactions between participan ts. We invite submissions of recent work, includi ng preliminary research, related to the theme of t he workshop. The Program Committee will select a s ubset of the abstracts for oral presentation. At l east one author of each accepted abstract is expec ted to represent it at the workshop (in person, or virtually). Note that accepted papers will be mad e available on the workshop website but will not b e 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-colum n pages long at most (plus at most four for biblio graphy and possible appendixes) and must be submit ted in the JMLR/PMLR format. We do accept submissi ons of work recently published or currently under review. X-ALT-DESC;FMTTYPE=text/html:
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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.

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The go al of this workshop is to bring together experts o n logic who could benefit from grammatical inferen ce tools, and researchers in grammatical inference who could find in logic and verification new frui tful applications for their methods. The LearnAut workshop will consist of 3 invited talks and 14 co ntributed talks from researchers whose submitted w orks were selected after a double-blind peer-revie wed phase. A significant amount of time will be ke pt for interactions between participants.

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We invite submissions of recent wor k, including preliminary research, related to the theme of the workshop. The Program Committee will select a subset of the abstracts for oral presenta tion. At least one author of each accepted abstrac t is expected to represent it at the workshop (in person, or virtually). 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). Submissions in t he form of extended abstracts must be at most 8 si ngle-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 o f work recently published or currently under revie w.

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URL:https://learnaut22.github.io END:VEVENT END:VCALENDAR