@inproceedings{AzzMazRicRig25-IJCAI-IC,
title = {Most Probable Explanation in Probabilistic Answer Set Programming},
author = {Azzolini, Damiano and Mazzotta, Giuseppe and Ricca, Francesco and Riguzzi, Fabrizio},
booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-25}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {James Kwok},
pages = {9049--9057},
year = {2025},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2025/1006}
}
@inproceedings{AzzMazRicRig25-KR-IC,
author = {Damiano Azzolini and Giuseppe Mazzotta and Francesco Ricca and Fabrizio Riguzzi},
title = {A Novel Framework for Reasoning over Optimization Problems in Probabilistic Answer Set Programming},
booktitle = {22nd International Conference on Principles of Knowledge Representation and Reasoning},
year = {2025}
}
@inproceedings{AzzMazRicRig25-ECAI-IC,
author = {Damiano Azzolini and Giuseppe Mazzotta and Francesco Ricca and Fabrizio Riguzzi},
title = {An Algebraic View of {MAP} Inference in Probabilistic Answer Set Programs},
booktitle = {ECAI 2025 28th - European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy - Including 14th Conference on Prestigious Applications of Intelligent Systems (PAIS 2025)},
year = {2025},
doi = {10.3233/FAIA250972},
volume = {413},
series = {Frontiers in Artificial Intelligence and Applications},
publisher = {IOS Press}
}
@article{BizChuJai25-JISA-IJ,
title = {Neurosymbolic AI for network intrusion detection systems: A survey},
journal = {Journal of Information Security and Applications},
volume = {94},
pages = {104205},
year = {2025},
issn = {2214-2126},
doi = {10.1016/j.jisa.2025.104205},
url = {https://www.sciencedirect.com/science/article/pii/S221421262500242X},
author = {Alice Bizzarri and Chung-En (Johnny) Yu and Brian Jalaian and Fabrizio Riguzzi and Nathaniel D. Bastian},
keywords = {Neurosymbolic artificial intelligence, Network intrusion detection system, Cybersecurity, Artificial intelligence}
}
@article{AzzRicSwi25-ICLP-IJ,
author = {Damiano Azzolini and Fabrizio Riguzzi and Theresa Swift},
title = {Integrating Belief Domains into Probabilistic Logic Programming},
journal = {Theory and Practice of Logic Programming},
year = {2025},
doi = {10.1017/S1471068425100161}
}
@inproceedings{ManGia25-IJCLR-IC,
title = {Benchmarking in Neuro-Symbolic AI},
author = {Manhaeve, Robin and Giannini, Francesco and Ali, Mehdi and Azzolini, Damiano and Bizzarri, Alice and Borghesi, Andrea and Bortolotti, Samuele and De Raedt, Luc and Dhami, Devendra and Diligenti, Michelangelo and Sebastijan Dumancic and Boi Faltings and Elisabetta Gentili and Alfonso Gerevini and Marco Gori and Tias Guns and Martin Homola and Kristian Kersting and Jens Lehmann and Michele Lombardi and Luca Lorello and Emanuele Marconato and Stefano Melacci and Andrea Passerini and Debjit Paul and Fabrizio Riguzzi and Stefano Teso and Neil Yorke-Smith and Marco Lippi},
booktitle = {Proceedings of The 4th International Joint Conference on Learning \& Reasoning},
year = {2025}
}
@article{AlbLamRigZes25-AI-IJ,
title = {A Semantics for Probabilistic Hybrid Knowledge Bases with Function Symbols},
author = {Marco Alberti and Evelina Lamma and Fabrizio Riguzzi and Riccardo Zese},
journal = {Artificial Intelligence},
doi = {10.1016/j.artint.2025.104361},
volume = {346},
pages = {104361},
year = {2025},
issn = {0004-3702},
url = {https://www.sciencedirect.com/science/article/pii/S0004370225000803},
keywords = {Hybrid knowledge bases, Minimal knowledge with negation as failure, Probability, Distribution semantics},
abstract = {Hybrid Knowledge Bases (HKBs) successfully integrate Logic Programming (LP) and Description Logics (DL) under the Minimal Knowledge with Negation as Failure semantics. Both world closure assumptions (open and closed) can be used in the same HKB, a feature required in many domains, such as the legal and health-care ones. In previous work, we proposed (function-free) Probabilistic HKBs, whose semantics applied Sato's distribution semantics approach to the well-founded HKB semantics proposed by Knorr et al. and Lyu and You. This semantics relied on the fact that the grounding of a function-free Probabilistic HKB (PHKB) is finite. In this article, we extend the PHKB language to allow function symbols, obtaining PHKBFS. Because the grounding of a PHKBFS can be infinite, we propose a novel semantics which does not require the PHKBFS's grounding to be finite. We show that the proposed semantics extends the previously proposed semantics and that, for a large class of PHKBFS, every query can be assigned a probability.}
}
@inproceedings{BizYouJalRigBas24-AIXIA-IC,
author = {Bizzarri, Alice
and Yu, Chung-En
and Jalaian, Brian
and Riguzzi, Fabrizio
and Bastian, Nathaniel D.},
editor = {Artale, Alessandro
and Cortellessa, Gabriella
and Montali, Marco},
title = {Neuro-Symbolic Integration for Open Set Recognition in Network Intrusion Detection},
booktitle = {AIxIA 2024 -- Advances in Artificial Intelligence},
year = {2025},
publisher = {Springer Nature Switzerland},
address = {Cham},
pages = {50--63},
abstract = {Open Set Recognition (OSR) addresses the challenge of classifying inputs into known and unknown categories, a crucial task where labeling is often prohibitively expensive or incomplete. This is particularly vital in applications like Network Intrusion Detection Systems (NIDS), where OSR is used to identify novel, previously unknown attacks. We propose a neuro-symbolic integration approach that combines deep learning and symbolic methods, enhancing deep embedding for clustering with custom loss functions and leveraging XGBoost's decision tree algorithms. Our methodology not only robustly addresses the identification of previously unknown attacks in NIDS but also effectively manages scenarios involving covariance shift. We demonstrate the efficacy of our approach through extensive experimentation, achieving an AUROC of 0.99 in both contexts. This paper presents a significant step forward in OSR for network intrusion detection by integrating deep and symbolic learning to handle unforeseen challenges in dynamic environments.},
isbn = {978-3-031-80607-0},
doi = {10.1007/978-3-031-80607-0_5}
}
@article{AzzBelKieRig25-TPLP-IJ,
title = {Solving Decision Theory Problems with Probabilistic Answer Set Programming},
author = {Damiano Azzolini and Elena Bellodi and Rafael Kiesel and Fabrizio Riguzzi},
year = {2025},
journal = {Theory and Practice of Logic Programming},
publisher = {Cambridge University Press},
doi = {10.1017/S1471068424000474}
}
@inproceedings{SamGhaRig25-CILC-NC,
title = {An Evaluation of Open Source LLMs for Neuro-Symbolic Integration},
author = {Stefano Sambri and Atefeh Ghanbari and Fabrizio Riguzzi},
year = {2024},
editor = {Dario Guidotti and Laura Pandolfo and Luca Pulina},
booktitle = {Proceedings of the 40th Italian Conference on Computational Logic,(CILC2025)},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
address = {Aachen, Germany},
issn = {1613-0073},
venue = {Alghero, Italy},
pages = {1--14},
volume = {4003},
url = {https://ceur-ws.org/Vol-4003/paper03.pdf}
}
@article{ZesLamRig25-LMCS-IJ,
title = {Exploiting Uncertainty for Querying Inconsistent Description Logics Knowledge Bases},
author = {Riccardo Zese and Evelina Lamma and Fabrizio Riguzzi},
url = {https://lmcs.episciences.org/11476},
doi = {10.46298/lmcs-21(3:14)2025},
journal = {Logical Methods in Computer Science},
issn = {1860-5974},
volume = {Volume 21, Issue 3},
eid = 14,
year = {2025},
month = {Aug},
keywords = {Artificial Intelligence, Logic in Computer Science}
}
This file was generated by bibtex2html 1.98.