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[22]
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Damiano Azzolini, Elena Bellodi, Rafael Kiesel, and Fabrizio Riguzzi.
Solving decision theory problems with probabilistic answer set
programming.
Theory and Practice of Logic Programming, 2024.
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[21]
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Fabrizio Riguzzi.
Quantum algorithms for weighted constrained sampling and weighted
model counting.
Quantum Machine Intelligence, 6(2):73, 2024.
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DOI ]
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[20]
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Alice Bizzarri, Brian Jalaian, Fabrizio Riguzzi, and Nathaniel D. Bastian.
A neuro-symbolic artificial intelligence network intrusion detection
system.
In International Conference on Computer Communications and
Networks, ICCCN, 2024.
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[19]
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Damiano Azzolini and Fabrizio Riguzzi.
Probabilistic answer set programming with discrete and continuous
random variables.
Theory and Practice of Logic Programming, pages 1--32, 2024.
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DOI |
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[18]
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Damiano Azzolini and Fabrizio Riguzzi.
Fast inference for probabilistic answer set programs via the residual
program.
Theory and Practice of Logic Programming, 2024.
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[17]
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Damiano Azzolini, Elisabetta Gentili, and Fabrizio Riguzzi.
Symbolic parameter learning in probabilistic answer set programming.
Theory and Practice of Logic Programming, 2024.
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[16]
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Damiano Azzolini, Matteo Bonato, Elisabetta Gentili, and Fabrizio Riguzzi.
Logic programming for knowledge graph completion.
In Emanuele De Angelis and Maurizio Proietti, editors,
Proceedings of the 39th Italian Conference on Computational Logic
(CILC2024), volume 3733 of CEUR Workshop Proceedings, pages 1--14,
Aachen, Germany, 2024. Sun SITE Central Europe.
[ bib |
.pdf ]
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[15]
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Fabrizio Riguzzi.
Quantum algorithms for weighted constrained sampling and weighted
model counting.
arXiv, abs/2407.12816, 2024.
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http |
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[14]
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Alice Bizzarri, Michele Fraccaroli, Evelina Lamma, and Fabrizio Riguzzi.
Integration between constrained optimization and deep networks: a
survey.
Frontiers in Artificial Intelligence, 7, 2024.
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DOI |
http ]
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[13]
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Damiano Azzolini and Fabrizio Riguzzi.
Inference in probabilistic answer set programs with imprecise
probabilities via optimization.
In Negar Kiyavash and Joris M. Mooij, editors, Proceedings of
the Fortieth Conference on Uncertainty in Artificial Intelligence, volume
244 of Proceedings of Machine Learning Research, pages 225--234. PMLR,
15--19 Jul 2024.
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[12]
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Arnaud Nguembang Fadja, Giuseppe Cota, Francesco Bertasi, Fabrizio Riguzzi,
Enzo Losi, Lucrezia Manservigi, Mauro Venturini, and Giovanni Bechini.
Machine learning approaches for the prediction of gas turbine
transients.
Journal of Computer Science, 20(5):495--510, Feb 2024.
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DOI |
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[11]
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Damiano Azzolini, Elena Bellodi, and Fabrizio Riguzzi.
Learning the parameters of probabilistic answer set programs.
In Stephen H. Muggleton and Alireza Tamaddoni-Nezhad, editors,
Inductive Logic Programming - ILP 2022, volume 14363 of Lecture Notes
in Computer Science, pages 1--14, Cham, 2024. Springer Nature Switzerland.
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DOI |
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[10]
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Elisabetta Gentili, Alice Bizzarri, Damiano Azzolini, Riccardo Zese, and
Fabrizio Riguzzi.
Regularization in probabilistic inductive logic programming.
In Elena Bellodi, Francesca Alessandra Lisi, and Riccardo Zese,
editors, Inductive Logic Programming - ILP 2023, volume 14363 of
Lecture Notes in Computer Science, pages 16--29, Cham, 2023. Springer Nature
Switzerland.
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[9]
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Damiano Azzolini, Elena Bellodi, and Fabrizio Riguzzi.
Summary of statistical statements in probabilistic logic programming.
volume 385, pages 384 -- 385, 2023.
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DOI |
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[8]
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Damiano Azzolini, Elena Bellodi, and Fabrizio Riguzzi.
Towards a representation of decision theory problems with
probabilistic answer set programs.
volume 385, pages 190 -- 191, 2023.
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DOI |
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[7]
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Damiano Azzolini and Fabrizio Riguzzi.
Inference in probabilistic answer set programming under the credal
semantics.
In Roberto Basili, Domenico Lembo, Carla Limongelli, and Andrea
Orlandini, editors, AIxIA 2023 - Advances in Artificial Intelligence,
volume 14318 of Lecture Notes in Artificial Intelligence, pages
367--380, Heidelberg, Germany, 2023. Springer.
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DOI |
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[6]
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Damiano Azzolini and Fabrizio Riguzzi.
Lifted inference for statistical statements in probabilistic answer
set programming.
International Journal of Approximate Reasoning, 163:109040,
2023.
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DOI |
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[5]
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Tom Schrijvers, Birthe Van Den Berg, and Fabrizio Riguzzi.
Automatic differentiation in prolog.
Theory and Practice of Logic Programming, 23(4):900–917,
2023.
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DOI |
.pdf ]
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[4]
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Damiano Azzolini, Elisabetta Gentili, and Fabrizio Riguzzi.
Link prediction in knowledge graphs with probabilistic logic
programming: Work in progress.
In Joaquín Arias, Sotiris Batsakis, Wolfgang Faber, Gopal Gupta,
Francesco Pacenza, Emmanuel Papadakis, Livio Robaldo, Kilian Ruckschloss,
Elmer Salazar, Zeynep G. Saribatur, Ilias Tachmazidis, Felix Weitkamper, and
Adam Wyner, editors, Proceedings of the International Conference on
Logic Programming 2023 Workshops co-located with the 39th International
Conference on Logic Programming (ICLP 2023), volume 3437 of CEUR
Workshop Proceedings, pages 1--4. CEUR-WS.org, 2023.
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[3]
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Damiano Azzolini, Elena Bellodi, and Fabrizio Riguzzi.
MAP inference in probabilistic answer set programs.
In Agostino Dovier, Angelo Montanari, and Andrea Orlandini, editors,
AIxIA 2022 -- Advances in Artificial Intelligence, pages 413--426,
Cham, 2023. Springer International Publishing.
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DOI |
http ]
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[2]
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Damiano Azzolini, Elena Bellodi, and Fabrizio Riguzzi.
Approximate inference in probabilistic answer set programming for
statistical probabilities.
In Agostino Dovier, Angelo Montanari, and Andrea Orlandini, editors,
AIxIA 2022 -- Advances in Artificial Intelligence, pages 33--46, Cham,
2023. Springer International Publishing.
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DOI |
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[1]
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Salvatore Greco, Alessandro Salatiello, Nicolò Fabbri, Fabrizio Riguzzi,
Emanuele Locorotondo, Riccardo Spaggiari, Alfredo De Giorgi, and Angelina
Passaro.
Rapid assessment of COVID-19 mortality risk with GASS
classifiers.
Biomedicines, 11(3), 2023.
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