NAI journal issues

Neurosymbolic Artificial Intelligence is a continous publication journal where articles are published online as soon as they have completed the production process at the publisher. They are immediately available, under an open access license, in a fully citable form with a universal digital object identifier (DOI). Pre-prints of accepted articles, also referred to as Author Accepted Manuscripts (AAM) are also published online at this site both during and after the peer review stage. The official, published versions of accepted papers can be found on the publisher's NAI journal site.

Accepted papers:

Inaugural issue: Vision Papers by Editorial Board Members

Sun, R. (2024). Dual-process theories, cognitive architectures, and hybrid neural-symbolic models. In A. Oltramari (Ed.), Neurosymbolic Artificial Intelligence (pp. 1–9). IOS Press
Hitzler, P., Rayan, R., Zalewski, J., Saki Norouzi, S., Eberhart, A., Vassermann, E. (2024). Deep Deductive Reasoning is a Hard Deep Learning Problem. In G. Šír (Ed.), Neurosymbolic AI, 13 pages
Šír , G. (2024). A Computational Perspective on Neural-Symbolic Integration . In D. Silver (Ed.), Neurosymbolic AI, 12 pages
Fanizzi, N., d’Amato, C. (2024). The Blessing of Dimensionality . In L. Lamb (Ed.), Neurosymbolic AI, 15 pages
J. Wagner, B., d’Avlia Garcez, A., (2024), A Neurosymbolic Approach to AI Alignment. In L. De Raedt (Ed.), Neurosymbolic AI, 12 pages
Alam, M., Harmelen, FV., Acosta, M., (2024), Towards Semantically Enriched Embeddings for Knowledge Graph Completion . In E. Jimenez-Ruiz (Ed.), Neurosymbolic AI, 17 pages
Mileo, A., (2024), Towards a Neuro-Symbolic Cycle for Human-centered Explainability. In B. Wagner(Ed.), Neurosymbolic AI, 12 pages
Lee, JH., Lanza, S., Wermter, S., (2024), From Neural Activations to Concepts: A Survey on Explaining Concepts in Neural Networks. In F. Ilievski (Ed.), Neurosymbolic AI, 10 pages
Confalonieri, R,. Guizzardi, G., (2024), On the Multiple Roles of Ontologies in Explanations for Neuro-symbolic AI . In A. Mileo (Ed.), Neurosymbolic AI, 15 pages
Giunchiglia, E., Imrie , F., van der Schaar, M., Lukasiewicz, T., (2024), Machine Learning with Requirements: A Manifesto. In A. Ten Teije (Ed.), Neurosymbolic AI, 12 pages
Herron, D., Jiménez-Ruiz, E., Weyde, T., (2024), On the Potential of Logic and Reasoning in Neurosymbolic Systems using OWL-based Knowledge Graphs. In R. Confalonieri (Ed.), Neurosymbolic AI, 15 pages
Sabou, M., Llugiqi, M., J. Ekaputra, F., Waltersdorfer, L., Tsaneva, S., Knowledge Engineering in the Age of Neurosymbolic Systems. In D.Gormann (Ed.), Neurosymbolic AI, 19 pages
Singh, G., Tommasini, R., Bhatia, S., Mutharaju, R., Benchmarking Neuro-Symbolic Description Logic Reasoners: Existing Challenges and A Way Forward. In F. Rossi(Ed.), Neurosymbolic AI, 12 pages
Belle, V., On the relevance of logic for AI, and the promise of neuro-symbolic learning. In FV. Harmelen(Ed.), Neurosymbolic AI, 31 pages

Regular papers:

Jaleed Khan, M., Ilievski, F., G. Breslin, J., Curry, E. (2024). A Survey of Neurosymbolic Visual Reasoning with Scene Graphs and Common Sense Knowledge . In M. Sarker (Ed.), Neurosymbolic Artificial Intelligence, 24 pages
Sha, J., Shindo, H., Kersting, K., Dhami, DS., (2024). Neuro-Symbolic Predicate Invention: Learning Relational Concepts from Visual Scenes . In A. Russo (Ed.), Neurosymbolic AI, 26 pages
Hersche, M., Terzic, A., Karunaratne , G., Langenegger , J., Pouget, A., Cherubini , G., Benini, L., Sebastian, A., Rahimi, A., (2024), Factorizers for Distributed Sparse Block Codes. In Q. Chao (Ed.), Neurosymbolic AI, 23 pages
Dai, Y., Sharrof, S., De Kamps, M., Graph-ic Improvements: Adding Explicit Syntactic Graphs to Neural Machine Translation. In L. Lamb (Ed.), Neurosymbolic AI, 21 pages

Special issue with revised papers from the 18th International Conference on Neural-Symbolic Learning and Reasoning (NeSy2024):