<div dir="ltr"><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">***************************************************************</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">CALL FOR PAPERS (ESANN 2026)</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">European Symposium on Artificial Neural Networks, Computational</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">Intelligence and Machine Learning: </span><a target="_blank" href="https://www.esann.org/" class="editor-rtfLink" style="background:transparent;margin-top:0pt;margin-bottom:0pt;color:rgb(74,110,224)"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">https://www.esann.org/</span></a></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">More details on the Special Session "Neuro Symbolic AI and Complex Data": </span><a target="_blank" href="https://www.esann.org/special-sessions#session1" class="editor-rtfLink" style="background:transparent;margin-top:0pt;margin-bottom:0pt;color:rgb(74,110,224)"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">https://www.esann.org/special-sessions#session1</span></a></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">Bruges, Belgium, 22-24 April 2026</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">***************************************************************</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt"><br></span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">****** Call For Papers ******</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">In the contemporary era of Artificial Intelligence (AI) based decision-making, the application of AI </span><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">on</span><span style="background:transparent;margin-top:0pt;margin-bottom:0pt"> complex data (e.g., nonlinear systems, images, text, sequences, trees, and graphs) has become increasingly pivotal</span><span style="background:transparent;margin-top:0pt;margin-bottom:0pt"> - </span><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">spanning domains such as drug discovery, industrial automation, and decision support systems.</span><span style="background:transparent;margin-top:0pt;margin-bottom:0pt"> Yet, purely data-driven methods often fall short in domains where structured reasoning, interpretability, and the integration of human knowledge are essential. Neuro-symbolic AI emerges as a promising paradigm that combines the strengths of symbolic reasoning with sub-symbolic learning, bridging the gap between data-driven models and domain-specific knowledge, requirements, and constraints. </span><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">This fusion </span><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">allows for</span><span style="background:transparent;margin-top:0pt;margin-bottom:0pt"> more generalizable, explainable, and trustworthy systems</span><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">, </span><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">capable of incorporating</span><span style="background:transparent;margin-top:0pt;margin-bottom:0pt"> logical rules, expert knowledge, and domain constraints into complex data-driven tasks. </span><span style="background-color:transparent">In this context, Neuro-Symbolic AI holds the potential to enhance model sustainability, robustness, transparency, and alignment with human-centric goals. This additional knowledge can take many forms, for example: </span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">-> Constraint-Aware AI, which </span><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">embeds</span><span style="background:transparent;margin-top:0pt;margin-bottom:0pt"> hard or soft logical constraints or verification rules in learning algorithms;</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">-> AI for science, where (possibly interpretable) models must comply with physical laws or symbolic expressions;</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">-> Socially responsible AI, where ethical frameworks and cultural principles shape decisions;</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">-> Applications (e.g., bioinformatics, software engineering, natural sciences, or legal informatics) where knowledge graphs and ontologies guide data-driven inference.</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">This special session aims to gather valuable contributions and early findings in the field of Neuro-Symbolic AI for Complex Data. Our main objective is to showcase the potential and limitations of new ideas, improvements, and cross-disciplinary integrations of symbolic reasoning and machine learning for solving real-world problems. We welcome contributions across disciplines and encourage submissions that integrate symbolic reasoning, statistical learning, complex data, and domain-specific knowledge to advance the frontiers of AI research.</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt"><br></span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">****** Important Dates ******</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">Paper Submissions: 19 November 2025</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">Paper Acceptance Notifications: 23 January 2026</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">Conference: 22-24 April 2026</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt"><br></span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">****** Session Organisers ******</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">-> Luca Oneto (University of Genoa, Italy)</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">-> Nicolò Navarin (University of Padua,  Italy)</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">-> Luca Pasa (University of Padua,  Italy)</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">-> Davide Rigoni (University of Padua,  Italy)</span></p><p style="color:rgb(14,16,26);background:transparent;margin-top:0pt;margin-bottom:0pt"><em style="background:transparent;margin-top:0pt;margin-bottom:0pt"><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">-> </span></em><span style="background:transparent;margin-top:0pt;margin-bottom:0pt">Davide Anguita (DIBRIS - University of Genova, Italy)<br></span></p></div>