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.
Accepted papers:
- 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
- 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
- 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
- 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
- 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. Jiminez-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