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165 results on '"Calegari, Roberta"'

1. Hierarchical Knowledge Extraction from Opaque Machine Learning Predictors

Author

Sabbatini, Federico, Calegari, Roberta, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Artale, Alessandro, editor, Cortellessa, Gabriella, editor, and Montali, Marco, editor

Published
2025
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2. ICE: An Evaluation Metric to Assess Symbolic Knowledge Quality

Author

Sabbatini, Federico, Calegari, Roberta, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Artale, Alessandro, editor, Cortellessa, Gabriella, editor, and Montali, Marco, editor

Published
2025
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3. Proceedings 39th International Conference on Logic Programming

Author

Pontelli, Enrico, Costantini, Stefania, Dodaro, Carmine, Gaggl, Sarah, Calegari, Roberta, Garcez, Artur D'Avila, Fabiano, Francesco, Mileo, Alessandra, Russo, Alessandra, and Toni, Francesca

Subjects
Computer Science - Artificial Intelligence, Computer Science - Logic in Computer Science, Computer Science - Programming Languages, Computer Science - Symbolic Computation
Abstract

This volume contains the Technical Communications presented at the 39th International Conference on Logic Programming (ICLP 2023), held at Imperial College London, UK from July 9 to July 15, 2023. Technical Communications included here concern the Main Track, the Doctoral Consortium, the Application and Systems/Demo track, the Recently Published Research Track, the Birds-of-a-Feather track, the Thematic Tracks on Logic Programming and Machine Learning, and Logic Programming and Explainability, Ethics, and Trustworthiness.

Published
2023
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5. Evaluation Metrics for Symbolic Knowledge Extracted from Machine Learning Black Boxes: A Discussion Paper

Author

Sabbatini, Federico and Calegari, Roberta

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

As opaque decision systems are being increasingly adopted in almost any application field, issues about their lack of transparency and human readability are a concrete concern for end-users. Amongst existing proposals to associate human-interpretable knowledge with accurate predictions provided by opaque models, there are rule extraction techniques, capable of extracting symbolic knowledge out of an opaque model. However, how to assess the level of readability of the extracted knowledge quantitatively is still an open issue. Finding such a metric would be the key, for instance, to enable automatic comparison between a set of different knowledge representations, paving the way for the development of parameter autotuning algorithms for knowledge extractors. In this paper we discuss the need for such a metric as well as the criticalities of readability assessment and evaluation, taking into account the most common knowledge representations while highlighting the most puzzling issues., Comment: 2nd International Workshop on Explainable AI in Finance (XAI-FIN 2022)

Published
2022

6. Clustering-Based Approaches for Symbolic Knowledge Extraction

Author

Sabbatini, Federico and Calegari, Roberta

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Opaque models belonging to the machine learning world are ever more exploited in the most different application areas. These models, acting as black boxes (BB) from the human perspective, cannot be entirely trusted if the application is critical unless there exists a method to extract symbolic and human-readable knowledge out of them. In this paper we analyse a recurrent design adopted by symbolic knowledge extractors for BB regressors - that is, the creation of rules associated with hypercubic input space regions. We argue that this kind of partitioning may lead to suboptimal solutions when the data set at hand is high-dimensional or does not satisfy symmetric constraints. We then propose a (deep) clustering-based approach to be performed before symbolic knowledge extraction to achieve better performance with data sets of any kind., Comment: Third Workshop on Explainable Logic-Based Knowledge Representation (XLoKR2022)

Published
2022

8. Achieving Complete Coverage with Hypercube-Based Symbolic Knowledge-Extraction Techniques

Author

Sabbatini, Federico, Calegari, Roberta, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nowaczyk, Sławomir, editor, Biecek, Przemysław, editor, Chung, Neo Christopher, editor, Vallati, Mauro, editor, Skruch, Paweł, editor, Jaworek-Korjakowska, Joanna, editor, Parkinson, Simon, editor, Nikitas, Alexandros, editor, Atzmüller, Martin, editor, Kliegr, Tomáš, editor, Schmid, Ute, editor, Bobek, Szymon, editor, Lavrac, Nada, editor, Peeters, Marieke, editor, van Dierendonck, Roland, editor, Robben, Saskia, editor, Mercier-Laurent, Eunika, editor, Kayakutlu, Gülgün, editor, Owoc, Mieczyslaw Lech, editor, Mason, Karl, editor, Wahid, Abdul, editor, Bruno, Pierangela, editor, Calimeri, Francesco, editor, Cauteruccio, Francesco, editor, Terracina, Giorgio, editor, Wolter, Diedrich, editor, Leidner, Jochen L., editor, Kohlhase, Michael, editor, and Dimitrova, Vania, editor

Published
2024
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12. Burden of Persuasion in Argumentation

Author

Calegari, Roberta and Sartor, Giovanni

Subjects
Computer Science - Logic in Computer Science, D.1.6, I.2.3, I.2.5
Abstract

This paper provides a formal model for the burden of persuasion in dialogues, and in particular, in legal proceedings. The model shows how an allocation of the burden of persuasion may induce single outcomes in dialectical contexts in which, without such an allocation, the status of conflicting arguments would remain undecided. Our approach is based on a two-stage labelling. The first-stage labelling determines what arguments are accepted, rejected or undecided, regardless of the allocation of burden. The second-stage labelling revises the dialectical status of first-stage undecided arguments, according to burdens of persuasion. The labelling is finally extended in such a way as to obtain a complete labelling. Our model combines two ideas that have emerged in the debate on the burden of persuasion: the idea that the burden of persuasion determines the solution of conflicts between arguments, and the idea that its satisfaction depends on the dialectical status of the arguments concerned. Our approach also addresses inversions of the burden of persuasion, namely, cases in which the burden of persuasion over an argument does not extend to its subarguments., Comment: In Proceedings ICLP 2020, arXiv:2009.09158

Published
2020
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13. Modeling Contrary-to-Duty with CP-nets

Author

Calegari, Roberta, Loreggia, Andrea, Lorini, Emiliano, Rossi, Francesca, and Sartor, Giovanni

Subjects
Computer Science - Artificial Intelligence, Computer Science - Logic in Computer Science
Abstract

In a ceteris-paribus semantics for deontic logic, a state of affairs where a larger set of prescriptions is respected is preferable to a state of affairs where some of them are violated. Conditional preference nets (CP-nets) are a compact formalism to express and analyse ceteris paribus preferences, which nice computational properties. This paper shows how deontic concepts can be captured through conditional preference models. A restricted deontic logic will be defined, and mapped into conditional preference nets. We shall also show how to model contrary to duties obligations in CP-nets and how to capture in this formalism the distinction between strong and weak permission.

Published
2020

14. Bottom-Up and Top-Down Workflows for Hypercube- And Clustering-Based Knowledge Extractors

Author

Sabbatini, Federico, Calegari, Roberta, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Calvaresi, Davide, editor, Najjar, Amro, editor, Omicini, Andrea, editor, Aydogan, Reyhan, editor, Carli, Rachele, editor, Ciatto, Giovanni, editor, Mualla, Yazan, editor, and Främling, Kary, editor

Published
2023
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15. Multi-agent Cooperative Argumentation in Arg2P

Author

Pisano, Giuseppe, Calegari, Roberta, Omicini, Andrea, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Dovier, Agostino, editor, Montanari, Angelo, editor, and Orlandini, Andrea, editor

Published
2023
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16. The PSyKE Technology for Trustworthy Artificial Intelligence

Author

Calegari, Roberta, Sabbatini, Federico, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Dovier, Agostino, editor, Montanari, Angelo, editor, and Orlandini, Andrea, editor

Published
2023
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17. Modular Logic Argumentation in Arg-tuProlog

Author

Calegari, Roberta, Contissa, Giuseppe, Pisano, Giuseppe, Sartor, Galileo, Sartor, Giovanni, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bandini, Stefania, editor, Gasparini, Francesca, editor, Mascardi, Viviana, editor, Palmonari, Matteo, editor, and Vizzari, Giuseppe, editor

Published
2022
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18. Burden of Persuasion in Meta-argumentation

Author

Pisano, Giuseppe, Calegari, Roberta, Omicini, Andrea, Sartor, Giovanni, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bandini, Stefania, editor, Gasparini, Francesca, editor, Mascardi, Viviana, editor, Palmonari, Matteo, editor, and Vizzari, Giuseppe, editor

Published
2022
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19. Logic Programming as a Service

Author

Calegari, Roberta, Denti, Enrico, Mariani, Stefano, and Omicini, Andrea

Subjects
Computer Science - Artificial Intelligence, Computer Science - Programming Languages, Computer Science - Software Engineering, 68T27, I.2.11
Abstract

New generations of distributed systems are opening novel perspectives for logic programming (LP): on the one hand, service-oriented architectures represent nowadays the standard approach for distributed systems engineering; on the other hand, pervasive systems mandate for situated intelligence. In this paper we introduce the notion of Logic Programming as a Service (LPaaS) as a means to address the needs of pervasive intelligent systems through logic engines exploited as a distributed service. First we define the abstract architectural model by re-interpreting classical LP notions in the new context; then we elaborate on the nature of LP interpreted as a service by describing the basic LPaaS interface. Finally, we show how LPaaS works in practice by discussing its implementation in terms of distributed tuProlog engines, accounting for basic issues such as interoperability and configurability.

Published
2018
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24. Perspectives and Challenges of Telemedicine and Artificial Intelligence in Pediatric Dermatology.

Author

Zama, Daniele, Borghesi, Andrea, Ranieri, Alice, Manieri, Elisa, Pierantoni, Luca, Andreozzi, Laura, Dondi, Arianna, Neri, Iria, Lanari, Marcello, and Calegari, Roberta

Subjects
SKIN disease treatment, HEALTH services accessibility, DECISION support systems, ARTIFICIAL intelligence, DERMATOLOGY, DECISION making, TELEMEDICINE, PEDIATRICS, VIDEOCONFERENCING, DERMATOLOGISTS, MACHINE learning, TIME, COMMUNICATION barriers, CHILDREN
Abstract

Background: Pediatric dermatology represents one of the most underserved subspecialties in pediatrics. Artificial intelligence (AI) and telemedicine have become considerable in dermatology, reaching diagnostic accuracy comparable to or exceeding that of in-person visits. This work aims to review the current state of telemedicine and AI in pediatric dermatology, suggesting potential ways to address existing issues and challenges. Methods: We conducted a literature review including only articles published in the last 15 years. A total of 458 studies were identified, of which only 76 were included. Results: Most of the studies on telemedicine evaluate accuracy focused on concordance, which ranges from 70% to 89% for the most common pediatric skin diseases. Telemedicine showed the potential to manage chronic dermatological conditions in children, as well as decrease waiting times, and represents the chance for unprivileged populations to overcome barriers limiting access to medical care. The main limitations of telemedicine consist of the language barrier and the need for adequate technologies and acceptable image-quality video, which can be overcome by AI. AI-driven apps and platforms can facilitate remote consultations between pediatric dermatologists and patients or their caregivers. However, the integration of AI into clinical practice faces some challenges ranging from technical to ethical and regulatory. It is crucial to ensure that the development, deployment, and utilization of AI systems conform to the seven fundamental requirements for trustworthy AI. Conclusion: This study supplies a detailed discussion of open challenges with a particular focus on equity and ethical considerations and defining possible concrete directions. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Burdens of Persuasion and Standards of Proof in Structured Argumentation

Author

Calegari, Roberta, Sartor, Giovanni, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Baroni, Pietro, editor, Benzmüller, Christoph, editor, and Wáng, Yὶ N., editor

Published
2021
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26. Explainable and Ethical AI: A Perspective on Argumentation and Logic Programming

Author

Calegari, Roberta, Omicini, Andrea, Sartor, Giovanni, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Baldoni, Matteo, editor, and Bandini, Stefania, editor

Published
2021
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27. Lazy Stream Manipulation in Prolog via Backtracking: The Case of 2P-Kt

Author

Ciatto, Giovanni, Calegari, Roberta, Omicini, Andrea, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Faber, Wolfgang, editor, Friedrich, Gerhard, editor, Gebser, Martin, editor, and Morak, Michael, editor

Published
2021
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31. AEQUITAS. WP7. T7.3. Hiring Data For STEM Field. v1.0

Author

Ciatto, Giovanni, Parletta, Daniela-Angela, Calegari, Roberta, Borghesi, Andrea, Ciatto, Giovanni, Parletta, Daniela-Angela, Calegari, Roberta, and Borghesi, Andrea

Abstract

This dataset contains information about the hiring process conducted by Akkodis for job positions and candidates belonging to the STEM field. The data of the candidates have been thoroughly anonymized. The data contains the curricula of candidates and the details of the job positions to which they were matched. The candidates and job positions are explicitly selected among the STEM field (including but not limited: computer science, engineering, physics, mathematics, chemistry, etc.). More detailed information can be found in the README file included in the compressed archive. The goal of the data is to allow the study of bias in terms of: 1) class imbalanced dataset in favour of the election of a specific gender, age, race, social background, thus against a selected group of people or minorities; 2) interpretation (recruiter) bias. This activity is part of the HORIZON-CL4-2021-HUMAN-01-24-AEQUITAS project (g.a. 101070363). The aim of AEQUITAS to address and tackle the multiple manifestations of bias and unfairness in Artificial Intelligence (AI) from a variety of dimensions, such as the development of AI tools, the data used to train, test and validate them or the interpretation practices developed around them.

Published
2024

32. Engineering Micro-intelligence at the Edge of CPCS: Design Guidelines

Author

Calegari, Roberta, Ciatto, Giovanni, Denti, Enrico, Omicini, Andrea, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Montella, Raffaele, editor, Ciaramella, Angelo, editor, Fortino, Giancarlo, editor, Guerrieri, Antonio, editor, and Liotta, Antonio, editor

Published
2019
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33. Injecting (Micro)Intelligence in the IoT: Logic-Based Approaches for (M)MAS

Author

Omicini, Andrea, Calegari, Roberta, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lin, Donghui, editor, Ishida, Toru, editor, Zambonelli, Franco, editor, and Noda, Itsuki, editor

Published
2019
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34. Compliance checking on first-order knowledge with conflicting and compensatory norms: a comparison among currently available technologies.

Author

Robaldo, Livio, Batsakis, Sotiris, Calegari, Roberta, Calimeri, Francesco, Fujita, Megumi, Governatori, Guido, Morelli, Maria Concetta, Pacenza, Francesco, Pisano, Giuseppe, Satoh, Ken, Tachmazidis, Ilias, and Zangari, Jessica

Subjects
REASONING, FIRST-order logic, TECHNOLOGY, MACHINE learning
Abstract

This paper analyses and compares some of the automated reasoners that have been used in recent research for compliance checking. Although the list of the considered reasoners is not exhaustive, we believe that our analysis is representative enough to take stock of the current state of the art in the topic. We are interested here in formalizations at the first-order level. Past literature on normative reasoning mostly focuses on the propositional level. However, the propositional level is of little usefulness for concrete LegalTech applications, in which compliance checking must be enforced on (large) sets of individuals. Furthermore, we are interested in technologies that are freely available and that can be further investigated and compared by the scientific community. In other words, this paper does not consider technologies only employed in industry and/or whose source code is non-accessible. This paper formalizes a selected use case in the considered reasoners and compares the implementations, also in terms of simulations with respect to shared synthetic datasets. The comparison will highlight that lot of further research still needs to be done to integrate the benefits featured by the different reasoners into a single standardized first-order framework, suitable for LegalTech applications. All source codes are freely available at https://github.com/liviorobaldo/compliancecheckers, together with instructions to locally reproduce the simulations. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Proceedings 39th International Conference on Logic Programming

Author

Costantini, Stefania, primary, Pontelli, Enrico, additional, Calegari, Roberta, additional, Dodaro, Carmine, additional, Fabiano, Francesco, additional, Gaggl, Sarah, additional, Garcez, Artur, additional, Mileo, Alessandra, additional, Russo, Alessandra, additional, and Toni, Francesca, additional

Published
2023
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44. Untying black boxes with clustering-based symbolic knowledge extraction

Author

Sabbatini, Federico and Calegari, Roberta

Abstract

Machine learning black boxes, exemplified by deep neural networks, often exhibit challenges in interpretability due to their reliance on complicated relationships involving numerous internal parameters and input features. This lack of transparency from a human perspective renders their predictions untrustworthy, particularly in critical applications. In this paper, we address this issue by introducing the design and implementation of CReEPy, an algorithm for symbolic knowledge extraction based on explainable clustering. Specifically, CReEPy leverages the underlying clustering performed by the ExACT or CREAM algorithms to generate human-interpretable Prolog rules that mimic the behaviour of opaque models. Additionally, we introduce CRASH, an algorithm for the automated tuning of hyper-parameters required by CReEPy. We present experiments evaluating both the human readability and predictive performance of the proposed knowledge-extraction algorithm, employing existing state-of-the-art techniques as benchmarks for comparison in real-world applications.

Published
2024
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45. The Butlers Framework for Socio-Technical Smart Spaces

Author

Calegari, Roberta, Denti, Enrico, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bagnoli, Franco, editor, Satsiou, Anna, editor, Stavrakakis, Ioannis, editor, Nesi, Paolo, editor, Pacini, Giovanna, editor, Welp, Yanina, editor, Tiropanis, Thanassis, editor, and DiFranzo, Dominic, editor

Published
2016
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49. AEQUITAS. WP7. USE CASE HR1. DESC. v1.0

Author

Cantiani, Elena, Calegari, Roberta, Ciatto, Giovanni, Caliò, Sebastiano, Borghesi, Andrea, Cantiani, Elena, Calegari, Roberta, Ciatto, Giovanni, Caliò, Sebastiano, and Borghesi, Andrea

Abstract

The dataset contains the matching of job positions and hiring candidates; this data has been collected by a big Italian company, working in the HR sector - ADECCO. The detailed description of the data can be found in a README file within the compressed archive. This activity is part of the HORIZON-CL4-2021-HUMAN-01-24-AEQUITAS project (g.a. 101070363). The aim of AEQUITAS to address and tackle the multiple manifestations of bias and unfairness in Artificial Intelligence (AI) from a variety of dimensions, such as the development of AI tools, the data used to train, test and validate them or the interpretation practices developed around them. AEQUITAS offers an overall approach for tackling the problem, savant of the criticalities that automation and AI techniques bring about. This case study allows for detecting hiring dataset biases which are the primary source for training a novel AI system. For example, there are historical trends in the labour market in favour of men (higher levels of education, once of employment, hence of hiring) that might be reflected in the data history of ADECCO. Balancing these inequalities in data or leaving data biased and targeting debiasing or bias reducing algorithms is a key step for a fair AI system and dataset. The ADECCO data offer data were to compare selection decisions with regards to important bias, such as gender, age, economic background, etc.

Published
2023

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