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CALL FOR PAPERS (ESANN 2026)
European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning: https://www.esann.org/
More details on the Special Session “Neuro Symbolic AI and Complex Data”: https://www.esann.org/special-sessions#session1
Bruges, Belgium, 22-24 April 2026
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****** Call For Papers ******
In the contemporary era of Artificial Intelligence (AI) based decision-making, the application of AI on complex data (e.g., nonlinear systems, images, text, sequences, trees, and graphs) has become increasingly pivotal – spanning domains such as drug discovery, industrial automation, and decision support systems. 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. This fusion allows for more generalizable, explainable, and trustworthy systems, capable of incorporating logical rules, expert knowledge, and domain constraints into complex data-driven tasks. 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:
-) Constraint-Aware AI, which embeds hard or soft logical constraints or verification rules in learning algorithms;
-) AI for science, where (possibly interpretable) models must comply with physical laws or symbolic expressions;
-) Socially responsible AI, where ethical frameworks and cultural principles shape decisions;
-) Applications (e.g., bioinformatics, software engineering, natural sciences, or legal informatics) where knowledge graphs and ontologies guide data-driven inference.
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.
****** Session Organisers ******
-) Luca Oneto (University of Genoa, Italy)
-) Nicolò Navarin (University of Padua, Italy)
-) Luca Pasa (University of Padua, Italy)
-) Davide Rigoni (University of Padua, Italy)
-) Davide Anguita (DIBRIS – University of Genova, Italy)