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<h5>Description:</h5>
<p>The cDMN solver is an execution engine for the cDMN notation, based on a knowledge-based reasoning engine.&nbsp;</p>
<p>cDMN is an extension of the Decision Model and Notation (DMN) standard, a modelling standard for (straightforward) decision logic that aims to be user-friendly, and easily readable. We extended this notation by adding constraints and other related concepts, increasing the expressivity of the language while staying true to its original goal of user-friendliness.&nbsp;</p>
<p>The cDMN solver translates cDMN models into specifications for the IDP system &ndash; a state-of-the-art reasoning engine &ndash; which then executes the models. The IDP system is powerful enough to solve any cDMN model in reasonable amount of time.&nbsp;</p>

<h5>More Information:</h5>
<ul>
<li><strong>Grand Challenge/Workpackage(s)</strong>: <a href="/en/research/research-challenges/ai-driven-data-science" data-entry-id="58" target="_blank" rel="noopener">GC1</a> - WP2</li>
<li><strong>Responsible Research Lead</strong>: <a href="mailto:Joost.vennekens@kuleuven.be" target="_blank" rel="noopener">Joost Vennekens</a></li>
<li><strong>Authors</strong>: <a href="mailto:s.vandevelde@kuleuven.be" target="_blank" rel="noopener">Simon Vandevelde</a></li>
<li><strong>Programming Language</strong>: Python</li>
<li><a href="https://cdmn.readthedocs.io/en/latest/" target="_blank" rel="noopener">Link to the website</a></li>
<li><a href="https://gitlab.com/EAVISE/cdmn/cdmn-solver" target="_blank" rel="noopener">Link to the repository</a></li>
<li><strong>Categories</strong>: Symbolic AI</li>
</ul>

<h5>Publications:</h5>
<ul>
<li>
<p>B. Aerts, S. Vandevelde, J. Vennekens,&nbsp; "Tackling the DMN challenges with cDMN: A tight integration of DMN and constraint reasoning", in. Springer International Publishing (Proceedings of RuleML+RR 2020), pp. 23&ndash;38, 2020</p>
</li>
</ul>

<p>Solver for executing constraint Decision Model and Notation (cDMN) models.</p>