Per tipo

@article{skespaceresearch24,
   apice = {SkeSpaceresearch24},
   author = {Sabbatini, Federico and Catia Grimani and Calegari, Roberta},
   doi = {10.1016/j.asr.2024.04.041},
   iris = {11585/995846},
   journal = {Advances in Space Research},
   keywords = {Space interferometers, LISA, Machine learning, Explainable clustering},
   month = {July},
   number = 1,
   numpages = 13,
   pages = {505--517},
   title = {Bridging machine learning and diagnostics of the ESA LISA space mission with equation discovery via explainable artificial intelligence},
   url = {https://www.sciencedirect.com/science/article/pii/S0273117724003880},
   volume = 74,
   year = 2024
}

@article{ske-ia2024,
   apice = {SkeIa2024},
   author = {Sabbatini, Federico and Calegari, Roberta},
   doi = {10.3233/IA-240026},
   iris = {11585/995926},
   journal = {Intelligenza Artificiale},
   keywords = {Explainable clustering, explainable artificial intelligence, symbolic knowledge extraction, PSyKE},
   number = 1,
   numpages = 14,
   pages = {21--34},
   title = {Untying black boxes with clustering-based symbolic knowledge extraction},
   url = {https://journals.sagepub.com/doi/abs/10.3233/IA-240026},
   urlpdf = {https://journals.sagepub.com/doi/pdf/10.3233/IA-240026},
   volume = 18,
   year = 2024
}

@article{skemetrics-aaai2023,
   address = {San Francisco, California},
   apice = {SkemetricsAAAISpring2023},
   author = {Sabbatini, Federico and Calegari, Roberta},
   booktitle = {AAAI 2023 Spring Symposium Series},
   doi = {10.1007/s43681-023-00406-1},
   iris = {11585/995844},
   journal = {AI and Ethics},
   keywords = {Explainable artificial intelligence; Symbolic knowledge extraction; Readability metrics; AutoML},
   month = mar,
   publisher = {Springer Nature},
   title = {On the Evaluation of the Symbolic Knowledge Extracted from Black Boxes},
   year = 2023
}

@article{skeislr-csur56,
   acm = {3645103},
   apice = {SkeislrCsur56},
   articleno = 161,
   author = {Ciatto, Giovanni and Sabbatini, Federico and Agiollo, Andrea and Magnini, Matteo and Omicini, Andrea},
   dblp = {journals/csur/CiattoSAMO24},
   doi = {10.1145/3645103},
   eissn = {1557-734},
   iris = {11585/969235},
   issn = {0360-0300},
   journal = {ACM Computing Surveys},
   keywords = {Logic; Machine learning theory; Hybrid symbolic-numeric methods; Knowledge representation and reasoning},
   lens = {143-064-043-213-611},
   month = jun,
   number = 6,
   numpages = 35,
   openalex = {W4391645809},
   opencitations = {06804657002},
   pages = {1--35},
   publisher = {ACM},
   scholar = {13701373869146776438},
   scopus = {2-s2.0-85188835517},
   semanticscholar = {267611660},
   title = {Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review},
   url = {https://dl.acm.org/doi/10.1145/3645103},
   urlopenaccess = {https://dl.acm.org/doi/pdf/10.1145/3645103},
   urlpdf = {https://dl.acm.org/doi/pdf/10.1145/3645103},
   volume = 56,
   wos = {WOS:001208566200027},
   year = 2024
}

@article{hypercubeske-ia17,
   apice = {HypercubeSkeIa17},
   author = {Sabbatini, Federico and Ciatto, Giovanni and Calegari, Roberta and Omicini, Andrea},
   dblp = {journals/ia/SabbatiniCCO23},
   doi = {10.3233/IA-230001},
   editor = {Ferrando, Angelo and Mascardi, Viviana},
   iris = {11585/941033},
   journal = {Intelligenza Artificiale},
   keywords = {explainable AI, knowledge extraction, interpretable prediction, PSyKE},
   lens = {054-166-349-034-57X},
   month = jun,
   note = {Special issue: Selected papers from the 23rd Workshop ``From Objects to Agents'' (WOA 2022)},
   number = 1,
   numpages = 13,
   openalex = {W4380028559},
   pages = {63--75},
   publisher = {IOS Press},
   scholar = {14669296704428238758},
   scopus = {2-s2.0-85168332389},
   semanticscholar = {259324728},
   title = {Towards a Unified Model for Symbolic Knowledge Extraction with Hypercube-Based Methods},
   url = {https://content.iospress.com/articles/intelligenza-artificiale/ia230001},
   urlopenaccess = {https://cris.unibo.it/retrieve/3a2a510e-9dc5-4b07-b3f7-398cf2f21419/ia-2023-psyke.pdf},
   volume = 17,
   wos = {WOS:001424596800001},
   year = 2023
}

@article{psyke-ia16,
   apice = {PsykeIa16},
   author = {Sabbatini, Federico and Ciatto, Giovanni and Calegari, Roberta and Omicini, Andrea},
   dblp = {journals/ia/SabbatiniCCO22},
   doi = {10.3233/IA-210120},
   editor = {Calegari, Roberta and Ciatto, Giovanni and Omicini, Andrea and Vizzari, Giuseppe},
   eissn = {2211-0097},
   iris = {11585/890822},
   issn = {1724-8035},
   journal = {Intelligenza Artificiale},
   keywords = {Explainable AI, knowledge extraction, interpretable prediction, PSyKE},
   lens = {106-894-542-499-045},
   month = jul,
   number = 1,
   numpages = 22,
   openalex = {W4284967716},
   pages = {27--48},
   publisher = {IOS Press},
   scholar = {7559675640918015038},
   scopus = {2-s2.0-85134193338},
   semanticscholar = {250400188},
   title = {Symbolic knowledge extraction from opaque {ML} predictors in {PSyKE}: Platform design \& experiments},
   url = {https://content.iospress.com/articles/intelligenza-artificiale/ia220141},
   volume = 16,
   wos = {WOS:000825367300003},
   year = 2022
}

@article{apj-recurrent2020,
   apice = {ApjRecurrent2020},
   author = {Grimani, Catia and Cesarini, Andrea and Fabi, Michele and Sabbatini, Federico and Telloni, Daniele and Villani, Mattia},
   doi = {10.3847/1538-4357/abbb90},
   journal = {The Astrophysical Journal},
   month = nov,
   number = 1,
   numpages = 1,
   pages = {64},
   publisher = {American Astronomical Society},
   title = {Recurrent Galactic Cosmic-Ray Flux Modulation in L1 and Geomagnetic Activity during the Declining Phase of the Solar Cycle 24},
   url = {https://doi.org/10.3847/1538-4357/abbb90},
   volume = 904,
   year = 2020
}

@article{apj-characteristics18,
   abstract = {Galactic cosmic-ray (GCR) energy spectra observed in the inner heliosphere are modulated by the solar activity, the solar polarity and structures of solar and interplanetary origin. A high counting rate particle detector (PD) aboard LISA Pathfinder, meant for subsystems diagnostics, was devoted to the measurement of GCR and solar energetic particle integral fluxes above 70 MeV n−1 up to 6500 counts s−1. PD data were gathered with a sampling time of 15 s. Characteristics and energy dependence of GCR flux recurrent depressions and of a Forbush decrease dated 2016 August 2 are reported here. The capability of interplanetary missions, carrying PDs for instrument performance purposes, in monitoring the passage of interplanetary coronal mass ejections is also discussed.},
   apice = {ApjCharacteristics18},
   author = {Armano, M. and Audley, H. and Baird, J. and Bassan, M. and Benella, S. and Binetruy, P. and Born, M. and Bortoluzzi, D. and Cavalleri, A. and Cesarini, A. and Cruise, A. M. and Danzmann, K. and de Deus Silva, K. and Diepholz, I. and Dixon, G. and Dolesi, R. and Fabi, M. and Ferraioli, L. and Ferroni, V. and Finetti, N. and Fitzsimons, E. D. and Freschi, M. and Gesa, L. and Gibert, F. and Giardini, D. and Giusteri, R. and Grimani, C. and Grzymisch, J. and Harrison, I. and Heinzel, G. and Hewitson, M. and Hollington, D. and Hoyland, D. and Hueller, M. and Inchausp{\'{e}}, H. and Jennrich, O. and Jetzer, P. and Karnesis, N. and Kaune, B. and Korsakova, N. and Killow, C. J. and Laurenza, M. and Lobo, J. A. and Lloro, I. and Liu, L. and L{\'{o}}pez-Zaragoza, J. P. and Maarschalkerweerd, R. and Mance, D. and Mart{\'{\i}}n, V. and Martin-Polo, L. and Martino, J. and Martin-Porqueras, F. and Mateos, I. and McNamara, P. W. and Mendes, J. and Mendes, L. and Nofrarias, M. and Paczkowski, S. and Perreur-Lloyd, M. and Petiteau, A. and Pivato, P. and Plagnol, E. and Ramos-Castro, J. and Reiche, J. and Robertson, D. I. and Rivas, F. and Russano, G. and Sabbatini, Federico and Slutsky, J. and Sopuerta, C. F. and Sumner, T. and Tellon, D. and Texier, D. and Thorpe, J. I. and Vetrugno, D. and Vitale, S. and Wanner, G. and Ward, H. and Wass, P. and Weber, W. J. and Wissel, L. and Wittchen, A. and Zambotti, A. and Zanoni, C. and Zweifel, P.},
   doi = {10.3847/1538-4357/aaa774},
   journal = {The Astrophysical Journal},
   month = feb,
   number = 2,
   pages = {113},
   publisher = {American Astronomical Society},
   title = {Characteristics and Energy Dependence of Recurrent Galactic Cosmic-Ray Flux Depressions and of a Forbush Decrease with {LISA} Pathfinder},
   url = {https://doi.org/10.3847/1538-4357/aaa774},
   volume = 854,
   year = 2018
}

@incollection{skemetrics-aixia2024,
   apice = {SkemetricsAixia2024},
   author = {Sabbatini, Federico and Calegari, Roberta},
   doi = {10.1007/978-3-031-80607-0_19},
   iris = {11585/1018915},
   keywords = {Explainable artificial intelligence, Symbolic knowledge extraction, AutoML},
   numpages = 16,
   pages = {241--256},
   title = {ICE: An Evaluation Metric to Assess Symbolic Knowledge Quality},
   url = {https://link.springer.com/10.1007/978-3-031-80607-0_19},
   year = 2025
}

@inproceedings{ske-aixia2024,
   abstract = {Adopting opaque machine learning predictors, which achieve very high predictive performance, often necessitates incorporating symbolic knowledge-extraction techniques. These techniques aim to explain the opaque predictions, thus making them applicable in high-stakes scenarios. The development of symbolic knowledge-extraction procedures is evolving alongside the dynamic machine learning landscape. However, there are recurring drawbacks that tend to be overlooked or addressed in a suboptimum way. Common examples include the non-exhaustiveness of the global explanations generated for a black-box predictor or the unwanted discretisation introduced in the prediction of continuous variables. To tackle these challenges, in this work, we introduce the HEx algorithm, its formalisation and its properties. This algorithm aims to obtain a symbolic, hierarchical representation of the knowledge acquired by opaque machine learning classifiers and regressors, always ensuring knowledge exhaustiveness and avoiding any output discretisation. Experiments demonstrating the superior capabilities of HEx compared to state-of-the-art competitors in terms of predictive performance, completeness, and human readability are presented.},
   apice = {SkeAixia2024},
   author = {Sabbatini, Federico and Calegari, Roberta},
   booktitle = {Advances in Artificial Intelligence},
   doi = {10.1007/978-3-031-80607-0_20},
   iris = {11585/1018918},
   keywords = {Explainable artificial intelligence, Symbolic knowledge extraction, PSyKE},
   publisher = {Springer, Cham},
   series = {Lecture Notes in Computer Science},
   subseries = {AIxIA 2024},
   title = {Hierarchical Knowledge Extraction from Opaque Machine Learning Predictors},
   url = {https://link.springer.com/10.1007/978-3-031-80607-0_20},
   volume = 15450,
   year = 2025
}

@inproceedings{skemetrics-woa2024,
   abstract = {In multi-agent systems, understanding the similarities and differences in agents' knowledge is essential for effective decision-making, coordination, and knowledge sharing. Current similarity metrics like cosine similarity, Jaccard similarity, and BERTScore are often too generic for comparing knowledge bases, overlooking critical aspects such as overlapping and fragmented boundaries, and varying domain densities. This paper introduces new specific similarity metrics for comparing knowledge bases, represented via symbolic knowledge. Our method compares local explanations of individual instances, preserving computational resources and providing a comprehensive evaluation of knowledge similarity. This approach addresses the limitations of existing metrics, enhancing the functionality and efficiency of multi-agent systems.},
   apice = {SkemetricsWoa2024},
   author = {Sabbatini, Federico and Christel Sirocchi and Calegari, Roberta},
   booktitle = {WOA 2024 – 25th Workshop "From Objects to Agents 2024"},
   editor = {Marco Alderighi and Matteo Baldoni and Cristina Baroglio and Roberto Micalizio and Stefano Tedeschi},
   iris = {11585/995843},
   keywords = {Multi-agent systems, Knowledge similarity, Symbolic knowledge},
   title = {Symbolic Knowledge Comparison: Metrics and Methodologies for Multi-Agent Systems},
   url = {https://ceur-ws.org/Vol-3735/paper_17.pdf},
   urlpdf = {https://ceur-ws.org/Vol-3735/paper_17.pdf},
   venue = {Bard, AO, Italy},
   volume = 3735,
   year = 2024
}

@inproceedings{ske-aequitas24,
   abstract = {This work explores the efficacy of symbolic knowledge-extraction (SKE) techniques in identifying biases and unfairness within opaque predictive models. Logic rules extracted from black-box predictors make it possible to verify if decisions are influenced by protected or sensitive features. In particular, the identifi- cation of biased or unfair decisions can be achieved through the evaluation of if-then rules, detecting the inclusion of protected and/or sensitive information in the rules' precondition. The effectiveness of SKE in this regard is demonstrated here by conducting various simulations on a well-known data set for loan grant prediction. Our findings highlight the potential of SKE as a valuable tool to reveal biases and discrimination in opaque predictions, ultimately contributing to the pursuit of fair and transparent decision-making systems.},
   apice = {SkeAequitas24},
   author = {Sabbatini, Federico and Calegari, Roberta},
   booktitle = {Proceedings of the 2nd Workshop on Fairness and Bias in AI co-located with 27th European Conference on Artificial Intelligence (ECAI 2024)},
   iris = {11585/1001068},
   keywords = {Fairness in AI, Bias in AI, Explainable artificial intelligence, XAI, Symbolic knowledge extraction, PSyKE},
   month = oct,
   publisher = {Sun SITE Central Europe, RWTH Aachen University},
   series = {CEUR Workshop Proceedings},
   title = {Unmasking the Shadows: Leveraging Symbolic Knowledge Extraction to Discover Biases and Unfairness in Opaque Predictive Models},
   url = {https://ceur-ws.org/Vol-3808/paper13.pdf},
   venue = {Santiago de Compostela, Spain},
   volume = 3808,
   year = 2024
}

@inproceedings{cream-kr2023,
   address = {Rhodes, Greece},
   apice = {CreamKr2023},
   author = {Sabbatini, Federico and Calegari, Roberta},
   booktitle = {20th International Conference on Principles of Knowledge Representation and Reasoning},
   doi = {10.24963/kr.2023/58},
   editor = {Pierre Marquis and Tran Cao Son and Gabriele Kern-Isberner},
   iris = {11585/893144},
   isbn = {978-1-956792-02-7},
   issn = {2334-1033},
   keywords = {Explainable AI, Applications that combine KR with machine learning, Integrating knowledge representation and machine learning, KR and machine learning, inductive logic programming, knowledge acquisition},
   month = aug,
   pages = {593--603},
   publisher = {IJCAI Organization},
   scopus = {2-s2.0-85176733868},
   title = {Explainable Clustering with {CREAM}},
   url = {https://proceedings.kr.org/2023/58/},
   urlpdf = {https://proceedings.kr.org/2023/58/kr2023-0058-sabbatini-et-al.pdf},
   year = 2023
}

@inproceedings{fire-ecai2023,
   address = {Krakov, Poland},
   apice = {FireEcai2023},
   author = {Sabbatini, Federico and Calegari, Roberta},
   booktitle = {26th European Conference on Artificial Intelligence, September 30–October 4, 2023, Kraków, Poland -- Including 12th Conference on Prestigious Applications of Intelligent Systems (PAIS 2023)},
   doi = {10.3233/FAIA230496},
   editor = {Kobi Gal and Ann Nowé and Grzegorz J. Nalepa and Roy Fairstein and Roxana Rădulescu},
   eissn = {978-1-64368-437-6},
   iris = {11585/952617},
   pages = {2033--2040},
   scopus = {2-s2.0-85175827763},
   series = {Frontiers in Artificial Intelligence and Applications},
   subseries = {ECAI 2023},
   title = {Symbolic Knowledge-Extraction Evaluation Metrics: The {FiRe} Score},
   url = {https://ebooks.iospress.nl/doi/10.3233/FAIA230496},
   volume = 372,
   year = 2023
}

@inproceedings{explainableclustering-woa2023,
   apice = {ExplainableclusteringWoa2023},
   author = {Sabbatini, Federico and Calegari, Roberta},
   booktitle = {WOA 2023 -- 24th Workshop ``From Objects to Agents''},
   editor = {Rino Falcone and Cristiano Castelfranchi and Alessandro Sapienza and Filippo Cantucci},
   iris = {11585/952626},
   issn = {1613-0073},
   keywords = {Explainable clustering, Explainable artificial intelligence, Symbolic knowledge extraction, PSyKE},
   month = nov,
   numpages = 14,
   pages = {232--245},
   publisher = {Sun SITE Central Europe, RWTH Aachen University},
   scopus = {2-s2.0-85179624285},
   series = {CEUR Workshop Proceedings},
   subseries = {AIxIA Series},
   title = {Unlocking Insights and Trust: The Value of Explainable Clustering Algorithms for Cognitive Agents},
   url = {https://ceur-ws.org/Vol-3579/paper18.pdf},
   urlopenaccess = {https://ceur-ws.org/Vol-3579/paper18.pdf},
   urlpdf = {https://ceur-ws.org/Vol-3579/paper18.pdf},
   volume = 3579,
   year = 2023
}

@incollection{psyketrust-aixia2022,
   address = {Cham, Switzerland},
   apice = {PsykeTrustAixia2022},
   author = {Calegari, Roberta and Federico, Sabbatini},
   booktitle = {AIxIA 2022},
   doi = {10.1007/978-3-031-27181-6_1},
   editor = {Dovier, Agostino and Montanari, Angelo and Orlandini, Andrea},
   eisbn = {978-3-031-27181-6},
   institution = {University of Udine},
   isbn = {978-3-031-27180-9},
   issn = {0302-9743},
   location = {Udine, Italy},
   month = mar,
   note = {XXI International Conference of the Italian Association for Artificial Intelligence, AIxIA 2022, Udine, Italy, November 28 -- December 2, 2022, Proceedings},
   numpages = 14,
   pages = {3--16},
   publisher = {Springer},
   series = {Lecture Notes in Computer Science},
   subseries = {Lecture Notes in Artificial Intelligence},
   title = {The {PSyKE} Technology for Trustworthy Artificial Intelligence},
   url = {https://doi.org/10.1007/978-3-031-27181-6_1},
   volume = 13796,
   year = 2023
}

@inproceedings{creepy-beware2023,
   address = {Rome, Italy},
   apice = {CreepyBeware2023},
   author = {Sabbatini, Federico and Calegari, Roberta},
   booktitle = {Proceedings of the 2nd Workshop on Bias, Ethical AI, Explainability and the Role of Logic and Logic Programming (BEWARE 2023)},
   iris = {11585/962357},
   keywords = {Explainable clustering, Explainable artificial intelligence, Symbolic knowledge extraction, PSyKE},
   scopus = {2-s2.0-85183205073},
   series = {CEUR Workshop Proceedings},
   subseries = {AIxIA Series},
   title = {Unveiling Opaque Predictors via Explainable Clustering: The {CReEPy} Algorithm},
   url = {https://ceur-ws.org/Vol-3615/paper1.pdf},
   volume = 3615,
   year = 2023
}

@inproceedings{exact-kodis2023,
   address = {Rhodes, Greece},
   apice = {ExactKodis2023},
   author = {Sabbatini, Federico and Calegari, Roberta},
   booktitle = {Proceedings of the 2nd International Workshop on Knowledge Diversity (KoDis 2023)},
   iris = {11585/962330},
   keywords = {Explainable clustering, Explainable artificial intelligence, PSyKE},
   pages = {3:1--3:8},
   scopus = {2-s2.0-85178369832},
   series = {CEUR Workshop Proceedings},
   title = {{ExACT} Explainable Clustering: Unravelling the Intricacies of Cluster Formation},
   url = {https://ceur-ws.org/Vol-3548/paper3.pdf},
   urlopenaccess = {https://ceur-ws.org/Vol-3548/paper3.pdf},
   urlpdf = {https://ceur-ws.org/Vol-3548/paper3.pdf},
   year = 2023
}

@inproceedings{completeness-ximl2023,
   apice = {CompletenessXiml2023},
   author = {Sabbatini, Federico and Calegari, Roberta},
   booktitle = {Artificial Intelligence. {ECAI} 2023 International Workshops -- {XAI}{\({^3}\)}, {TACTIFUL}, {XI-ML}, {SEDAMI}, {RAAIT}, {AI4S}, {HYDRA}, {AI4AI}, Krak{\'{o}}w, Poland, September 30 -- October 4, 2023, Proceedings, Part {I}},
   doi = {10.1007/978-3-031-50396-2_10},
   editor = {Nowaczyk, Slawomir and Biecek, Przemyslaw and Chung, Neo Christopher and Vallati, Mauro and Skruch, Pawel and Jaworek{-}Korjakowska, Joanna and Parkinson, Simon and Nikitas, Alexandros and Atzmüller, Martin and Kliegr, Tomás and others},
   iris = {11585/962344},
   keywords = {Symbolic knowledge extraction, Explainable artificial intelligence, PSyKE},
   pages = {179--197},
   publisher = {Springer},
   scopus = {2-s2.0-85184090360},
   series = {Communications in Computer and Information Science},
   title = {Achieving Complete Coverage with Hypercube-Based Symbolic Knowledge-Extraction Techniques},
   url = {https://link.springer.com/10.1007/978-3-031-50396-2_10},
   urlopenaccess = {https://link.springer.com/10.1007/978-3-031-50396-2_10},
   volume = 1947,
   year = 2023
}

@incollection{clustering-extraamas2023,
   address = {London, UK},
   apice = {ClusteringExtraamas2023},
   author = {Sabbatini, Federico and Calegari, Roberta},
   booktitle = {Explainable and Transparent AI and Multi-Agent Systems},
   doi = {10.1007/978-3-031-40878-6_7},
   editor = {Calvaresi, Davide and Najjar, Amro and Omicini, Andrea and Aydoǧan, Reyhan and Carli, Rachele and Ciatto, Giovanni and Mualla, Yazan and Främling, Kary},
   iris = {11585/962326},
   isbn = {978-3-031-40877-9},
   issn = {0302-9743},
   keywords = {Explainable artificial intelligence; Symbolic knowledge extraction; Clustering},
   pages = {116--129},
   publisher = {Springer},
   scopus = {2-s2.0-85172259054},
   series = {Lecture Notes in Computer Science},
   subseries = {Lecture Notes in Artificial Intelligence},
   title = {Bottom-Up and Top-Down Workflows for Hypercube- and Clustering-based Knowledge Extractors},
   url = {https://link.springer.com/10.1007/978-3-031-40878-6_7},
   volume = 14127,
   year = 2023
}

@incollection{hypercube-woa2022,
   apice = {HypercubeWoa2022},
   author = {Sabbatini, Federico and Ciatto, Giovanni and Calegari, Roberta and Omicini, Andrea},
   booktitle = {WOA 2022 -- 23rd Workshop ``From Objects to Agents''},
   dblp = {conf/woa/SabbatiniCCO22},
   editor = {Ferrando, Angelo and Mascardi, Viviana},
   iris = {11585/899358},
   issn = {1613-0073},
   keywords = {Explainable AI; Knowledge extraction; Interpretable prediction; PSyKE},
   month = nov,
   numpages = 13,
   pages = {48--60},
   publisher = {Sun SITE Central Europe, RWTH Aachen University},
   scholar = {8614662013642803891},
   scopus = {2-s2.0-85142519111},
   semanticscholar = {253270041},
   series = {CEUR Workshop Proceedings},
   subseries = {AIxIA Series},
   title = {Hypercube-Based Methods for Symbolic Knowledge Extraction: Towards a Unified Model},
   url = {http://ceur-ws.org/Vol-3261/paper4.pdf},
   urlopenaccess = {http://ceur-ws.org/Vol-3261/paper4.pdf},
   urlpdf = {http://ceur-ws.org/Vol-3261/paper4.pdf},
   volume = 3261,
   wos = {WOS:001788658400004},
   year = 2022
}

@inproceedings{skemetrics-xaifin2022,
   address = {New York, NY, USA},
   apice = {SkemetricsXaifin2022},
   author = {Sabbatini, Federico and Calegari, Roberta},
   booktitle = {Workshop on Explainable AI in Finance @ICAIF 2022},
   doi = {10.48550/arXiv.2211.00238},
   keywords = {Explainable artificial intelligence; Symbolic knowledge extraction; Readability metrics; AutoML},
   month = {November 2},
   scholar = {5021962022669458541},
   semanticscholar = {253244189},
   title = {Evaluation Metrics for Symbolic Knowledge Extracted from Machine Learning Black Boxes: A Discussion Paper},
   url = {https://arxiv.org/abs/2211.00238},
   urlopenaccess = {https://arxiv.org/abs/2211.00238},
   year = 2022
}

@incollection{swpsyke-extraamas2022,
   apice = {SwpsykeExtraamas2022},
   author = {Sabbatini, Federico and Ciatto, Giovanni and Omicini, Andrea},
   booktitle = {Explainable and Transparent AI and Multi-Agent Systems},
   chapter = 8,
   dblp = {conf/atal/SabbatiniCO22},
   doi = {10.1007/978-3-031-15565-9_8},
   editor = {Calvaresi, Davide and Najjar, Amro and Winikoff, Michael and Främling, Kary},
   iris = {11585/899474},
   isbn = {978-3-031-15564-2},
   keywords = {Explainable AI, Knowledge extraction, Semantic Web, Intelligent agents, PSyKE},
   lens = {195-560-268-268-743},
   numpages = 19,
   openalex = {W4297897402},
   pages = {124--142},
   publisher = {Springer},
   scholar = {11339767386934277898},
   scopus = {2-s2.0-85140488560},
   series = {Lecture Notes in Computer Science},
   subtitle = {4th International Workshop, EXTRAAMAS 2022, Virtual Event, May 9–10, 2022, Revised Selected Papers},
   title = {Semantic Web-Based Interoperability for Intelligent Agents with {PSyKE}},
   url = {https://link.springer.com/10.1007/978-3-031-15565-9_8},
   urlpdf = {https://link.springer.com/content/pdf/10.1007/978-3-031-15565-9_8.pdf},
   volume = 13283,
   wos = {WOS:000870042100008},
   year = 2022
}

@inproceedings{gridrex-kr2022,
   address = {Haifa, Israel},
   apice = {PsykeKr2022},
   author = {Sabbatini, Federico and Calegari, Roberta},
   booktitle = {19th International Conference on Principles of Knowledge Representation and Reasoning (KR 2022)},
   doi = {10.24963/kr.2022/57},
   editor = {Kern-Isberner, Gabriele and Lakemeyer, Gerhard and Meyer, Thomas},
   iris = {11585/893144},
   isbn = {978-1-956792-01-0},
   issn = {2334-1033},
   keywords = {Explainable AI, Integrating knowledge representation and machine learning, Integrating symbolic and sub-symbolic approaches, Applications of KR},
   month = {July 31--August 5},
   pages = {554--563},
   publisher = {IJCAI Organization},
   title = {Symbolic Knowledge Extraction from Opaque Machine Learning Predictors: {GridREx} {\&} {PEDRO}},
   url = {https://proceedings.kr.org/2022/57/},
   year = 2022
}

@inproceedings{cluster-ske-xlokr2022,
   address = {Haifa, Israel},
   apice = {ExplainableClusteringWsKr2022},
   author = {Sabbatini, Federico and Calegari, Roberta},
   booktitle = {XLoKR 2022 - 3rd Workshop on Explainable Logic-Based Knowledge Representation},
   keywords = {Explainable AI; Symbolic knowledge extraction; Clustering},
   month = {July 31},
   title = {Clustering-Based Approaches for Symbolic Knowledge Extraction},
   url = {https://arxiv.org/abs/2211.00234},
   year = 2022
}

@incollection{gridex-extraamas2021,
   address = {Cham, Switzerland},
   apice = {GridExExtraamas2021},
   author = {Sabbatini, Federico and Ciatto, Giovanni and Omicini, Andrea},
   booktitle = {Explainable and Transparent AI and Multi-Agent Systems. 3rd International Workshop, EXTRAAMAS 2021, Virtual Event, May 3--7, 2021, Revised Selected Papers},
   dblp = {conf/atal/SabbatiniCO21},
   doi = {10.1007/978-3-030-82017-6_2},
   editor = {Calvaresi, Davide and Najjar, Amro and Winikoff, Michael and Främling, Kary},
   eisbn = {978-3-030-82017-6},
   eissn = {1611-3349},
   iris = {11585/834616},
   isbn = {978-3-030-82016-9},
   issn = {0302-9743},
   keywords = {Explainable AI; Knowledge extraction; Interpretable prediction; Regression; Iter; GridEx},
   lens = {128-962-186-028-465},
   month = jul,
   numpages = 21,
   openalex = {W3184161979},
   pages = {18--38},
   publisher = {Springer Nature},
   scholar = {855045469053426346},
   scopus = {2-s2.0-85113335454},
   series = {Lecture Notes in Computer Science},
   subseries = {Lecture Notes in Artificial Intelligence},
   title = {{GridEx}: An Algorithm for Knowledge Extraction from Black-Box Regressors},
   url = {http://link.springer.com/10.1007/978-3-030-82017-6_2},
   urlopenaccess = {https://cris.unibo.it/retrieve/handle/11585/834616/830237/extraamas-2021-iter.pdf},
   urlpdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-82017-6_2.pdf},
   volume = 12688,
   wos = {WOS:000691781800002},
   year = 2021
}

@inproceedings{psyke-woa2021,
   apice = {PsykeWoa2021},
   articleno = 3,
   author = {Sabbatini, Federico and Ciatto, Giovanni and Calegari, Roberta and Omicini, Andrea},
   booktitle = {WOA 2021 -- 22nd Workshop ``From Objects to Agents''},
   dblp = {conf/woa/SabbatiniCCO21},
   editor = {Calegari, Roberta and Ciatto, Giovanni and Denti, Enrico and Omicini, Andrea and Sartor, Giovanni},
   iris = {11585/834364},
   issn = {1613-0073},
   keywords = {explainable AI, knowledge extraction, interpretable prediction, PSyKE},
   lens = {138-404-520-962-779},
   location = {Bologna, Italy},
   month = oct,
   note = {22nd Workshop ``From Objects to Agents'' (WOA 2021), Bologna, Italy, 1--3~} # sep # {~2021. Proceedings},
   numpages = 20,
   openalex = {W3204048750},
   pages = {29--48},
   publisher = {Sun SITE Central Europe, RWTH Aachen University},
   scholar = {879185583484020388},
   scopus = {2-s2.0-85116894019},
   series = {CEUR Workshop Proceedings},
   subseries = {AI*IA Series},
   title = {On the Design of {PSyKE}: A Platform for Symbolic Knowledge Extraction},
   url = {http://ceur-ws.org/Vol-2963/paper14.pdf},
   volume = 2963,
   wos = {WOS:001788669700003},
   year = 2021
}

@incollection{neurosymbolic-woa25y,
   address = {Cham},
   apice = {NeurosymbolicWoa25Y},
   author = {Agiollo, Andrea and Calegari, Roberta and Ciatto, Giovanni and Magnini, Matteo and Omicini, Andrea and Sabbatini, Federico},
   booktitle = {The Agents Journey: Twenty-Five Years of Multi-agent Systems},
   chapter = 12,
   doi = {10.1007/978-3-032-22940-3_12},
   editor = {Mascardi, Viviana and Omicini, Andrea},
   eisbn = {978-3-032-22940-3},
   eissn = {1611-3349},
   iris = {11585/1061411},
   isbn = {978-3-032-22939-7},
   issn = {0302-9743},
   keywords = {Rational Agents, Logic Programming, Multi-agent Systems, Symbolic-Subsymbolic Integration},
   lens = {028-851-073-271-306},
   month = apr,
   numpages = 20,
   openalex = {W7155969644},
   pages = {320--339},
   publisher = {Springer Nature Switzerland},
   scholar = {21797340038813643},
   scopus = {2-s2.0-105041239279},
   series = {Lecture Notes in Computer Science},
   subseries = {State-of-the-Art Survey},
   title = {Intelligent Agents from Symbolic to Neurosymbolic Systems: The Quest for Integration},
   url = {https://link.springer.com/10.1007/978-3-032-22940-3_12},
   urlopenaccess = {https://link.springer.com/content/pdf/10.1007/978-3-032-22940-3_12.pdf},
   volume = 16395,
   year = 2026
}