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#NeuroSymbolicAI

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isws

Our 3rd keynoter will be Enrico Motta from the Knowledge Media Institute of UK’s Open University. He pioneered with hybrid computational architectures integrating #ML with knowledge representation, being applied in application domains as e.g. news & scholarly analytics, and cognitive robotics.

Apply here: 2025.semanticwebschool.org/
Deadline: March 15, 2025

#knowledgegraph #AI #llm #generativeAI #responsibleAI @lysander07 @fiz_karlsruhe #semanticweb #lod #semanticweb #neurosymbolicai #robotics

Black and white portrait photography of Enrico Motta.
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Harald Sack

TGDK is launching a Special Issue for Use-Case articles. They are interested in submissions describing use-cases (applications, events, products, services, etc.) that put research on Graph Data & Knowledge into practice. If you work on such initiatives, consider telling the community about them by submitting to the Special Issue!
Deadline is March 31, 2025.

drops.dagstuhl.de/entities/jou

@tgdkjournal #knowledgegraphs #semanticweb #ontologies #AI #neurosymbolicAI

drops.dagstuhl.deTGDK
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José A. Alonso

Formally verified neurosymbolic trajectory learning via tensor-based linear temporal logic on finite traces. ~ Mark Chevallier, Filip Smola, Richard Schmoetten, Jacques D. Fleuriot. arxiv.org/abs/2501.13712 #ITP #IsabelleHOL #NeuroSymbolicAI

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arXiv.orgFormally Verified Neurosymbolic Trajectory Learning via Tensor-based Linear Temporal Logic on Finite TracesWe present a novel formalisation of tensor semantics for linear temporal logic on finite traces (LTLf), with formal proofs of correctness carried out in the theorem prover Isabelle/HOL. We demonstrate that this formalisation can be integrated into a neurosymbolic learning process by defining and verifying a differentiable loss function for the LTLf constraints, and automatically generating an implementation that integrates with PyTorch. We show that, by using this loss, the process learns to satisfy pre-specified logical constraints. Our approach offers a fully rigorous framework for constrained training, eliminating many of the inherent risks of ad-hoc, manual implementations of logical aspects directly in an "unsafe" programming language such as Python, while retaining efficiency in implementation.
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José A. Alonso

Neuro-Symbolic AI in 2024: A systematic review. ~ Brandon C. Colelough, William Regli. arxiv.org/abs/2501.05435 #AI #NeuroSymbolicAI

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arXiv.orgNeuro-Symbolic AI in 2024: A Systematic ReviewBackground: The field of Artificial Intelligence has undergone cyclical periods of growth and decline, known as AI summers and winters. Currently, we are in the third AI summer, characterized by significant advancements and commercialization, particularly in the integration of Symbolic AI and Sub-Symbolic AI, leading to the emergence of Neuro-Symbolic AI. Methods: The review followed the PRISMA methodology, utilizing databases such as IEEE Explore, Google Scholar, arXiv, ACM, and SpringerLink. The inclusion criteria targeted peer-reviewed papers published between 2020 and 2024. Papers were screened for relevance to Neuro-Symbolic AI, with further inclusion based on the availability of associated codebases to ensure reproducibility. Results: From an initial pool of 1,428 papers, 167 met the inclusion criteria and were analyzed in detail. The majority of research efforts are concentrated in the areas of learning and inference (63%), logic and reasoning (35%), and knowledge representation (44%). Explainability and trustworthiness are less represented (28%), with Meta-Cognition being the least explored area (5%). The review identifies significant interdisciplinary opportunities, particularly in integrating explainability and trustworthiness with other research areas. Conclusion: Neuro-Symbolic AI research has seen rapid growth since 2020, with concentrated efforts in learning and inference. Significant gaps remain in explainability, trustworthiness, and Meta-Cognition. Addressing these gaps through interdisciplinary research will be crucial for advancing the field towards more intelligent, reliable, and context-aware AI systems.
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