@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{AzzMazRicRig25-IJCAI-IC,
author = {Damiano Azzolini and Giuseppe Mazzotta and Francesco Ricca and Fabrizio Riguzzi},
title = {Most Probable Explanation in Probabilistic Answer Set Programming},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2025}
}
@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}
}
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