@inproceedings{ManGiaAli-24-IJCLR-IC,
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 Duman{\v{c}}i{\'{c}}, Sebastijan
and Faltings, Boi
and Gentili, Elisabetta
and Gerevini, Alfonso
and Gori, Marco
and Guns, Tias
and Homola, Martin
and Kersting, Kristian
and Lehmann, Jens
and Lombardi, Michele
and Lorello, Luca
and Marconato, Emanuele
and Melacci, Stefano
and Passerini, Andrea
and Paul, Debjit
and Riguzzi, Fabrizio
and Teso, Stefano
and Yorke-Smith, Neil
and Lippi, Marco},
editor = {Dai, Wang-Zhou},
title = {Benchmarking in Neuro-Symbolic AI},
booktitle = {Learning and Reasoning: 4th International Joint Conference on Learning and Reasoning, IJCLR 2024, and 33rd International Conference on Inductive Logic Programming, ILP 2024, Nanjing, China, September 20--22, 2024, Proceedings},
year = {2026},
publisher = {Springer Nature Switzerland},
address = {Cham},
pages = {238--249},
abstract = {Neural-symbolic (NeSy) AI has gained a lot of popularity by enhancing learning models with explicit reasoning capabilities. Both new systems and new benchmarks are constantly introduced and used to evaluate learning and reasoning skills. The large variety of systems and benchmarks, however, makes it difficult to establish a fair comparison among the various frameworks, let alone a unifying set of benchmarking criteria. This paper analyzes the state-of-the-art in benchmarking NeSy systems, studies its limitations, and proposes ways to overcome them. We categorize popular neural-symbolic frameworks into three groups: model-theoretic, proof-theoretic fuzzy, and proof-theoretic probabilistic systems. We show how these three categories have distinct strengths and weaknesses, and how this is reflected in the type of tasks and benchmarks to which they are applied.},
doi = {10.1007/978-3-032-09087-4_17},
isbn = {978-3-032-09087-4}
}
@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 = {{Proceedings of the 22nd International Conference on Principles of Knowledge Representation and Reasoning}},
pages = {67--77},
year = {2025},
month = {10},
doi = {10.24963/kr.2025/7}
}
@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{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{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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