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<h2>Welcome</h2>
<p>
    The fifth edition of "Learning and Automata" (LearnAut) will be held at <a href="https://compose.ioc.ee/icalp2024/">ICALP/LiCS/FSCD 2024</a> in Tallinn (Estonia), 7th of July 2024.
</p>

<p><span style="color: red">Update:</span>
It is possible to join this workshop online. This is free of charge, but you need to register via <a href="https://forms.gle/BtATReQNSVNEeJix9">this form</a>. After registration, we will send you a link to the Zoom call before Sunday morning. (If you have not received the Zoom link by then, please contact Joshua Moerman.)
</p>
<p><span style="color: red">Update 2:</span>
We thank everyone who attended and participated in the workshop!
</p>

<h2>Scope</h2>
<p>
Grammatical Inference (GI) studies machine learning algorithms for various language related models such as automata and grammars.
Historically, these models are used, for instance, to understand natural language and to do computational linguistics.
At the same time, these kind of models are also a major research topic within the ICALP community.
These models are central in understanding recursive computations and their expressive power and complexity.
In recent years we have seen some important results starting to bridge the gap between both worlds, including applications of learning to formal verification and model checking, (co-)algebraic formulations of automata and grammar learning algorithms and theoretical foundations of learning.
The aim of this workshop is to bring together experts on language theory that could benefit from grammatical inference tools, and researchers in grammatical inference who could find new insights for their methods in theoretical computer science.
</p>
<p>
The aim of this workshop is to bring together experts on language theory that could benefit from grammatical inference tools, and researchers in grammatical inference who could find new insights for their methods in theoretical computer science.
</p>

<h2>Content</h2>
<p>
The LearnAut workshop will consists of a number of invited talks, other talks from researchers who submitted their work to the workshop, and discussions.
An important amount of time will be kept for interactions between participants.
</p>

<h2>Previous editions</h2>
<ul>
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    <li><a href="{{ edition.url }}">{{ edition.desc }}</a></li>
    {% endfor %}
</ul>

<h2>Acknowledgment</h2>
<p>
This workshop is partially supported by the EPSRC project CLeVer (EP/S028641/1)
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