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1. 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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3. 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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4. 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

5. 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

9. 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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10. 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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11. 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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13. 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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14. 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

18. 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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19. 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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20. 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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22. 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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23. 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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24. 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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28. 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

29. 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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30. 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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31. 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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37. 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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38. 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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43. 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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46. 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

48. Arguing about the existence of conflicts

Author

Pisano, Giuseppe, Calegari, Roberta, Prakken, Henry, Sartor, Giovanni, Toni, Francesca, Polberg, Sylwia, Booth, Richard, Caminada, Martin, Kido, Hiroyuki, Sub Intelligent Systems, and Intelligent Systems

Subjects
argumentation, Artificial Intelligence, ASPIC+, meta-Argumentation
Abstract

In this paper we formalise a meta-argumentation framework as an ASPIC+ extension which enables reasoning about conflicts between formulae of the argumentation language. The result is a standard abstract argumentation framework that can be evaluated via grounded semantics.

Published
2022

50. Embedding s(CASP) in Prolog

Author

Wielemaker, Jan, Tiihonen, Mikko, Arias, Joaquin, Calegari, Roberta, Dickens, Luke, Faber, Wolfgang, Fandinno, Jorge, Gupta, Gopal, Hecher, Markus, Inclezan, Daniela, LeBlanc, Emily, Morak, Michael, Salazar, Elmer, Zangari, Jessica, Business Web and Media, Network Institute, and Intelligent Information Systems

Subjects
multi-paradigm, Prolog, answer set programming, s(CASP)
Abstract

The original s(CASP) implementation is a stand-alone program implemented in Ciao Prolog. It reads the s(CASP) source using a dedicated parser, resolves the query embedded in the source and emits the results in a format dictated by commandline options. Typical applications require composing a s(CASP) program, solving a query and reasoning about the bindings, model and/or justification. This is often done in some external language, e.g., Python. In this paper we propose a closer integration with Prolog. The s(CASP) program is simply a Prolog program that has to respect certain constraints. The scasp library can be used to solve a query using s(CASP) semantics, making the bindings available in the normal Prolog way and providing access to the model and justification as Prolog terms. This way, we can exploits Prolog’s power for manipulating (Prolog) terms for construction the s(CASP) program and interpreting the results.

Published
2022

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