arXiv:2607. 13073v1 Announce Type: new Abstract: Neuro-symbolic AI based on $IFOL_B$ is a way to combine neural learning and symbolic reasoning to overcome limitations of purely neural systems (like lack of interpretability and logical structure) with formal logical machinery for self-reference.
arXiv:2604. 09567v2 Announce Type: replace-cross Abstract: Knowledge representation formalisms are aimed to represent general conceptual information and are typically used in the construction of the knowledge base of reasoning agent.
arXiv:2608. 16565v1 Announce Type: new Abstract: This cumulative habilitation thesis studies probabilistic circuits (PCs) as a powerful and tractable framework for reasoning and learning under uncertainty in artificial intelligence (AI).
arXiv:2608. 04285v1 Announce Type: new Abstract: Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention.
arXiv:2407.09693v3 Announce Type: replace Abstract: The field of Neural-Symbolic (NeSy) systems is growing rapidly. Proposed approaches show great promise in achieving symbiotic unions of neural and...
arXiv:2608. 12325v1 Announce Type: new Abstract: Autonomous reasoning is among the most scientifically and economically motivating topics in AI today.
arXiv:2606. 23938v1 Announce Type: new Abstract: Driving VLA models incorporating Chain-of-Thought (CoT) reasoning are attractive because they leverage pretrained VLM representations and expose intermediate decisions in natural language, yet current rationales often lack the step-by-step decision semantics needed to keep the rationale causally connected to the planned motion.
arXiv:2608.29530v1 Announce Type: cross Abstract: Modern systems in artificial intelligence (AI) somehow excel in domains for which they seem poorly suited. Intelligence has traditionally been modele...
Baobab compiles an OWL 2 DL (ΣROIQ) ontology with a finite ABox into a Sentential Decision Diagram (SDD), saturating a propositional core and instantiating remaining DL features over the active domain. The resulting evidence‑conditioned weighted model count trains a perception network to recognize real images under partial ABox supervision, enabling a CNN to recover latent ontology concepts that an independent perception would miss. When supervision allows multiple ontology‑consistent completions, Baobab’s mixture indexed by query justifications represents the calibrated posterior, achieving Bayes‑optimal performance on a real‑image MNIST task where single‑WMC and learned mixtures fail, thereby characterizing and mitigating reasoning shortcuts in a non‑Horn description logic.
arXiv:2510. 14538v3 Announce Type: replace Abstract: Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.
The paper introduces a neuro‑symbolic architecture that combines symbolic planning, reinforcement learning, and a large language model (LLM) to address novelties in dynamic open‑world environments. The LLM is used to identify missing operators, generate symbolic plans, and write reward functions, enabling the reinforcement learning agent to learn control policies for newly identified operators. The proposed method outperforms state‑of‑the‑art approaches in both operator discovery and learning within continuous robotic domains.
arXiv:2605. 04193v2 Announce Type: replace Abstract: Inductive Logic Programming (ILP) aims to learn interpretable first-order rules from data, but existing symbolic and neuro-symbolic approaches struggle to scale to noisy and probabilistic settings.
arXiv:2608. 06839v1 Announce Type: new Abstract: Artificial Neural networks (ANNs) are often treated as black-box models, making explainability a central challenge in deep learning.