Motivation

The field of Neurosymbolic Artificial Intelligence (NeSy) promises to combine the scalability and adaptability of neural networks with the precision and interpretability of logic-based reasoning systems. Despite significant research progress, the development of NeSy systems that seamlessly integrate symbolic and sub-symbolic elements at scale for real-world practical applications remains an open challenge. Current NeSy research largely focuses on synthetic problems with complete rule sets in controlled environments, making assumptions that often don't hold in real-world scenarios.

As large language models and pure deep learning approaches continue to advance, we must ask: what unique value can neurosymbolic approaches offer? This Dagstuhl Seminar challenges the belief that scaling alone is sufficient for AI advancement and argues that systematic integration of symbolic and sub-symbolic methods is essential for building more capable, interpretable, and reliable AI systems that align with realworld application needs in critical domains.

The seminar aims to bring together researchers and practitioners from diverse backgrounds – including those working in robotics, healthcare, natural sciences, machine learning, and knowledge representation – to develop a concrete action plan for advancing practical Neurosymbolic AI applications over the next three to five years.

Key Challenges to Address

We have identified several key challenges that currently limit the practical adoption of NeSy systems. These include creating more realistic benchmarks and standardized evaluation metrics to better capture reasoning capabilities; identifying specific use cases in robotics, natural sciences, and healthcare where NeSy methods can deliver unique value; developing accessible open-source frameworks and tools to reduce entry barriers; creating user-friendly knowledge acquisition tools for domain experts who lack technical AI backgrounds or NeSy expertise; and developing educational resources to make NeSy principles more approachable for newcomers. The seminar welcomes discussion on these challenges as well as additional ones identified by participants, with the goal of collaboratively developing a roadmap that addresses the most pressing barriers to practical adoption.

Expected Outcomes

The seminar aims to produce several concrete outputs: a position paper outlining the roadmap for practical NeSy applications; plans for open-source libraries and tools; strategies for developing improved benchmarks; and potential collaborative funding proposals. By facilitating cross-domain collaboration between symbolic AI experts, deep learning researchers, and domain specialists, we aim to overcome the current gap between theoretical NeSy research and practical applications.

This timely seminar comes as the AI community increasingly recognizes the limitations of pure deep learning methods and seeks more robust, interpretable alternatives, particularly for safety-critical applications. Through structured discussions and collaborative breakout sessions, participants are expected to chart a path forward that leverages the complementary strengths of neural and symbolic approaches to address real-world challenges.

Creative Commons BY 4.0

Tarek Richard Besold, Leilani H. Gilpin, Kristian Kersting, Annette ten Teije, and Thiviyan Thanapalasingam