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170 results on '"CIATTO, GIOVANNI"'

1. Concurrency Model of BDI Programming Frameworks: Why Should We Control It?

Author

Baiardi, Martina, Burattini, Samuele, Ciatto, Giovanni, Pianini, Danilo, Omicini, Andrea, and Ricci, Alessandro

Subjects
Computer Science - Multiagent Systems
Abstract

We provide a taxonomy of concurrency models for BDI frameworks, elicited by analysing state-of-the-art technologies, and aimed at helping both BDI designers and developers in making informed decisions. Comparison among BDI technologies w.r.t. concurrency models reveals heterogeneous support, and low customisability.

Published
2024

2. On the external concurrency of current BDI frameworks for MAS

Author

Baiardi, Martina, Burattini, Samuele, Ciatto, Giovanni, Pianini, Danilo, Ricci, Alessandro, and Omicini, Andrea

Subjects
Computer Science - Multiagent Systems
Abstract

The execution of Belief-Desire-Intention (BDI) agents in a Multi-Agent System (MAS) can be practically implemented on top of low-level concurrency mechanisms that impact on efficiency, determinism, and reproducibility. We argue that developers should specify the MAS behaviour independently of the execution model, and choose or configure the concurrency model later on, according to the specific needs of their target domain, leaving the MAS specification unaffected. We identify patterns for mapping the agent execution over the underlying concurrency abstractions, and investigate which concurrency models are supported by some of the most commonly used BDI platforms. Although most frameworks support multiple concurrency models, we find that they mostly hide them under the hood, making them opaque to the developer, and actually limiting the possibility of fine-tuning the MAS.

Published
2024

3. Large language models as oracles for instantiating ontologies with domain-specific knowledge

Author

Ciatto, Giovanni, Agiollo, Andrea, Magnini, Matteo, and Omicini, Andrea

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Information Retrieval, Computer Science - Machine Learning, Computer Science - Logic in Computer Science
Abstract

Background. Endowing intelligent systems with semantic data commonly requires designing and instantiating ontologies with domain-specific knowledge. Especially in the early phases, those activities are typically performed manually by human experts possibly leveraging on their own experience. The resulting process is therefore time-consuming, error-prone, and often biased by the personal background of the ontology designer. Objective. To mitigate that issue, we propose a novel domain-independent approach to automatically instantiate ontologies with domain-specific knowledge, by leveraging on large language models (LLMs) as oracles. Method. Starting from (i) an initial schema composed by inter-related classes andproperties and (ii) a set of query templates, our method queries the LLM multiple times, and generates instances for both classes and properties from its replies. Thus, the ontology is automatically filled with domain-specific knowledge, compliant to the initial schema. As a result, the ontology is quickly and automatically enriched with manifold instances, which experts may consider to keep, adjust, discard, or complement according to their own needs and expertise. Contribution. We formalise our method in general way and instantiate it over various LLMs, as well as on a concrete case study. We report experiments rooted in the nutritional domain where an ontology of food meals and their ingredients is semi-automatically instantiated from scratch, starting from a categorisation of meals and their relationships. There, we analyse the quality of the generated ontologies and compare ontologies attained by exploiting different LLMs. Finally, we provide a SWOT analysis of the proposed method.

Published
2024

5. Fifty Years of Prolog and Beyond

Author

Körner, Philipp, Leuschel, Michael, Barbosa, João, Costa, Vítor Santos, Dahl, Verónica, Hermenegildo, Manuel V., Morales, Jose F., Wielemaker, Jan, Diaz, Daniel, Abreu, Salvador, and Ciatto, Giovanni

Subjects
Computer Science - Programming Languages
Abstract

Both logic programming in general, and Prolog in particular, have a long and fascinating history, intermingled with that of many disciplines they inherited from or catalyzed. A large body of research has been gathered over the last 50 years, supported by many Prolog implementations. Many implementations are still actively developed, while new ones keep appearing. Often, the features added by different systems were motivated by the interdisciplinary needs of programmers and implementors, yielding systems that, while sharing the "classic" core language, and, in particular, the main aspects of the ISO-Prolog standard, also depart from each other in other aspects. This obviously poses challenges for code portability. The field has also inspired many related, but quite different languages that have created their own communities. This article aims at integrating and applying the main lessons learned in the process of evolution of Prolog. It is structured into three major parts. Firstly, we overview the evolution of Prolog systems and the community approximately up to the ISO standard, considering both the main historic developments and the motivations behind several Prolog implementations, as well as other logic programming languages influenced by Prolog. Then, we discuss the Prolog implementations that are most active after the appearance of the standard: their visions, goals, commonalities, and incompatibilities. Finally, we perform a SWOT analysis in order to better identify the potential of Prolog, and propose future directions along which Prolog might continue to add useful features, interfaces, libraries, and tools, while at the same time improving compatibility between implementations., Comment: 87 pages, 2 figures. This article has been accepted for publication in Theory and Practice of Logic Programming (TPLP)

Published
2022

6. A General-Purpose Protocol for Multi-agent Based Explanations

Author

Ciatto, Giovanni, Magnini, Matteo, Buzcu, Berk, Aydoğan, Reyhan, 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, 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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7. JaKtA: BDI Agent-Oriented Programming in Pure Kotlin

Author

Baiardi, Martina, Burattini, Samuele, Ciatto, Giovanni, Pianini, Danilo, 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, Malvone, Vadim, editor, and Murano, Aniello, editor

Published
2023
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12. Semantic Web-Based Interoperability for Intelligent Agents with PSyKE

Author

Sabbatini, Federico, Ciatto, Giovanni, 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, Calvaresi, Davide, editor, Najjar, Amro, editor, Winikoff, Michael, editor, and Främling, Kary, editor

Published
2022
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13. On the Design of PSyKI: A Platform for Symbolic Knowledge Injection into Sub-symbolic Predictors

Author

Magnini, Matteo, Ciatto, Giovanni, 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, Calvaresi, Davide, editor, Najjar, Amro, editor, Winikoff, Michael, editor, and Främling, Kary, editor

Published
2022
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14. Expectation: Personalized Explainable Artificial Intelligence for Decentralized Agents with Heterogeneous Knowledge

Author

Calvaresi, Davide, Ciatto, Giovanni, Najjar, Amro, Aydoğan, Reyhan, Van der Torre, Leon, Omicini, Andrea, Schumacher, Michael, 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, Calvaresi, Davide, editor, Najjar, Amro, editor, Winikoff, Michael, editor, and Främling, Kary, editor

Published
2021
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15. Towards Explainable Visionary Agents: License to Dare and Imagine

Author

Ciatto, Giovanni, Najjar, Amro, Calbimonte, Jean-Paul, Calvaresi, Davide, 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, Calvaresi, Davide, editor, Najjar, Amro, editor, Winikoff, Michael, editor, and Främling, Kary, editor

Published
2021
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16. Shallow2Deep: Restraining Neural Networks Opacity Through Neural Architecture Search

Author

Agiollo, Andrea, Ciatto, Giovanni, 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, Calvaresi, Davide, editor, Najjar, Amro, editor, Winikoff, Michael, editor, and Främling, Kary, editor

Published
2021
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17. GridEx: An Algorithm for Knowledge Extraction from Black-Box Regressors

Author

Sabbatini, Federico, Ciatto, Giovanni, 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, Calvaresi, Davide, editor, Najjar, Amro, editor, Winikoff, Michael, editor, and Främling, Kary, editor

Published
2021
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18. 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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19. Smart Contracts are More than Objects: Pro-activeness on the Blockchain

Author

Ciatto, Giovanni, Maffi, Alfredo, Mariani, Stefano, Omicini, Andrea, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Prieto, Javier, editor, Das, Ashok Kumar, editor, Ferretti, Stefano, editor, Pinto, António, editor, and Corchado, Juan Manuel, editor

Published
2020
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20. Engineering Semantic Self-composition of Services Through Tuple-Based Coordination

Author

Caselli, Ashley, Ciatto, Giovanni, Di Marzo Serugendo, Giovanna, 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, and Margaria, Tiziana, editor

Published
2020
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21. Agent-Based Explanations in AI: Towards an Abstract Framework

Author

Ciatto, Giovanni, Schumacher, Michael I., Omicini, Andrea, Calvaresi, Davide, 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, Calvaresi, Davide, editor, Najjar, Amro, editor, Winikoff, Michael, editor, and Främling, Kary, editor

Published
2020
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24. 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

25. Comparative Analysis of Blockchain Technologies Under a Coordination Perspective

Author

Ciatto, Giovanni, Bosello, Michael, Mariani, Stefano, Omicini, Andrea, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, De La Prieta, Fernando, editor, González-Briones, Alfonso, editor, Pawleski, Pawel, editor, Calvaresi, Davide, editor, Del Val, Elena, editor, Lopes, Fernando, editor, Julian, Vicente, editor, Osaba, Eneko, editor, and Sánchez-Iborra, Ramón, editor

Published
2019
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26. 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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27. Towards Agent-Oriented Blockchains: Autonomous Smart Contracts

Author

Ciatto, Giovanni, Maffi, Alfredo, Mariani, Stefano, Omicini, Andrea, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Demazeau, Yves, editor, Matson, Eric, editor, Corchado, Juan Manuel, editor, and De la Prieta, Fernando, editor

Published
2019
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30. Programming the Interaction Space Effectively with

Author

Ciatto, Giovanni, Mariani, Stefano, Omicini, Andrea, Kacprzyk, Janusz, Series editor, Ivanović, Mirjana, editor, Bădică, Costin, editor, Dix, Jürgen, editor, Jovanović, Zoran, editor, Malgeri, Michele, editor, and Savić, Miloš, editor

Published
2018
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31. Twenty Years of Coordination Technologies: State-of-the-Art and Perspectives

Author

Ciatto, Giovanni, Mariani, Stefano, Louvel, Maxime, Omicini, Andrea, Zambonelli, Franco, 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, Di Marzo Serugendo, Giovanna, editor, and Loreti, Michele, editor

Published
2018
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43. Symbolic knowledge extraction for explainable nutritional recommenders

Author

Magnini, Matteo (author), Ciatto, Giovanni (author), Cantürk, Furkan (author), Aydoğan, Reyhan (author), Omicini, Andrea (author), Magnini, Matteo (author), Ciatto, Giovanni (author), Cantürk, Furkan (author), Aydoğan, Reyhan (author), and Omicini, Andrea (author)

Abstract

Background and objective:This paper focuses on nutritional recommendation systems (RS), i.e. AI-powered automatic systems providing users with suggestions about what to eat to pursue their weight/body shape goals. A trade-off among (potentially) conflictual requirements must be taken into account when designing these kinds of systems, there including: (i) adherence to experts’ prescriptions, (ii) adherence to users’ tastes and preferences, (iii) explainability of the whole recommendation process. Accordingly, in this paper we propose a novel approach to the engineering of nutritional RS, combining machine learning and symbolic knowledge extraction to profile users—hence harmonising the aforementioned requirements. MethodsOur contribution focuses on the data processing workflow. Stemming from neural networks (NN) trained to predict user preferences, we use CART Breiman et al.(1984) to extract symbolic rules in Prolog Körner et al.(2022) form, and we combine them with expert prescriptions brought in similar form. We can then query the resulting symbolic knowledge base via logic solvers, to draw explainable recommendations. ResultsExperiments are performed involving a publicly available dataset of 45,723 recipes, plus 12 synthetic datasets about as many imaginary users, and 6 experts’ prescriptions. Fully-connected 4-layered NN are trained on those datasets, reaching ∼86% test-set accuracy, on average. Extracted rules, in turn, have ∼80% fidelity w.r.t. those NN. The resulting recommendation system has a test-set precision of ∼74%. The symbolic approach makes it possible to devise how the system draws recommendations. ConclusionsThanks to our approach, intelligent agents may learn users’ preferences from data, convert them into symbolic form, and extend them with experts’ goal-directed prescriptions. The resulting recommendations are then simultaneously acceptable for the end user and adequate under a nutritional perspective, while the whole process of recommendatio, Interactive Intelligence

Published
2023
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44. 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

45. A General-Purpose Protocol for Multi-agent Based Explanations

Author

Ciatto, Giovanni (author), Magnini, Matteo (author), Buzcu, Berk (author), Aydoğan, Reyhan (author), Omicini, Andrea (author), Ciatto, Giovanni (author), Magnini, Matteo (author), Buzcu, Berk (author), Aydoğan, Reyhan (author), and Omicini, Andrea (author)

Abstract

Building on prior works on explanation negotiation protocols, this paper proposes a general-purpose protocol for multi-agent systems where recommender agents may need to provide explanations for their recommendations. The protocol specifies the roles and responsibilities of the explainee and the explainer agent and the types of information that should be exchanged between them to ensure a clear and effective explanation. However, it does not prescribe any particular sort of recommendation or explanation, hence remaining agnostic w.r.t. such notions. Novelty lays in the extended support for both ordinary and contrastive explanations, as well as for the situation where no explanation is needed as none is requested by the explainee. Accordingly, we formally present and analyse the protocol, motivating its design and discussing its generality. We also discuss the reification of the protocol into a re-usable software library, namely PyXMas, which is meant to support developers willing to build explainable MAS leveraging our protocol. Finally, we discuss how custom notions of recommendation and explanation can be easily plugged into PyXMas., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Interactive Intelligence

Published
2023
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46. Knowledge injection of Datalog rules via Neural Network Structuring with KINS.

Author

Magnini, Matteo, Ciatto, Giovanni, and Omicini, Andrea

Subjects
LOGIC
Abstract

We propose a novel method to inject symbolic knowledge in form of Datalog formulæ into neural networks (NN), called Knowledge Injection via Network Structuring (KINS). The idea behind our method is to extend NN internal structure with ad-hoc layers built out of the injected symbolic knowledge. KINS does not constrain NN to any specific architecture, neither requires logic formulæ to be ground. Moreover, it is robust w.r.t. both lack of data and imperfect/incomplete knowledge. Experiments are reported, involving multiple datasets and predictor types, to demonstrate how KINS can significantly improve the predictive performance of the neural networks it is applied to. [ABSTRACT FROM AUTHOR]

Published
2023
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50. On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge

Author

Omicini, Andrea, Ciatto, Giovanni <1992>, Omicini, Andrea, and Ciatto, Giovanni <1992>

Abstract

In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CL and DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacks and opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosys- tem can be reified into actual software technology and extended towards many DS-related directions.

Published
2022

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