Italy

PDDL+ is an expressive planning formalism that enables modelling of hybrid domains having both discrete and continuous dynamics, in which the agent and the environment description are sharply separated. Recently two mappings for translating a PDDL+ problem under discrete interpretation into a numeric task have been proposed. Such translations produce a task of exponential or polynomial size, with respect to the size of the native task. In this work, starting from the above-mentioned polynomial translation, we introduce a sound but not generally complete variant that has the potential to improve the performance of numeric planning engines. However, it is guaranteed to be complete only in a subclass of PDDL+ problems. We define the subclass of problems where the variant is safely applicable, and we empirically assess the advantages of such translation. Further, we show that even in cases when the variant does not guarantee the completeness, it can speed up the planning process, at the cost of losing part of the solutions space.

AIxIA 2021

A Sound (but Incomplete) Polynomial Translation from Discretised PDDL+ to Numeric Planning

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

technical paper

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The 20th International Conference of the Italian Association for Artificial Intelligence (**AIxIA 2021**) is organized by AIxIA (Associazione Italiana per l’Intelligenza Artificiale), which is a non-profit scientific society founded in 1988 and devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.

 
 



**General Chair**

Stefania Bandini, University of Milano-Bicocca, Italyand RCAST - The University of Tokyo, Japan


**Program Chairs**

Viviana Mascardi, University of Genova, Italy; Matteo Palmonari, University of Milano-Bicocca, Italy; Giuseppe Vizzari, University of Milano-Bicocca, Italy

**Organizing Committee**

Francesca Gasparini, University of Milano-Bicocca, Italy (**Chair**); Stefano Ferilli, University of Bari, Italy; Marta Giltri, University of Milano-Bicocca, Italy; Daniela Briola, University of Insubria, Italy


**Publicity and Communication Committee**

Elisabetta Fersini, University of Milano-Bicocca, Italy (**Chair**); Angelo Ferrando, University of Genova, Italy; Francesca Alessandra Lisi, University of Bari, Italy; Giovanni Stilo, University of L'Aquila, Italy; Marco Viviani, University of Milano-Bicocca, Italy


**Workshop Chairs**

Gabriella Cortellessa, ISTC-CNR, Rome, Italy; Silvia Rossi, University of Naples Federico II, Italy; Fabio Stella, University of Milano-Bicocca, Italy


**PC Members**

Fulvio Mastrogiovanni, University of Genova, Italy; Roberto Basili, University of Rome Tor Vergata, Italy; Nicola Gatti, Polytechnic University of Milan, Italy; Stefano Bistarelli, University of Perugia, Italy; Piero Poccianti, Consorzio Operativo Gruppo MPS, Italy; Francesco Scarcello, University of Calabria, Italy; Flavio S. Correa Da Silva, University of São Paulo, Brazil; Elena Cabrio, Université Côte d'Azur, CNRS, Inria, I3S, France; Antonio Lieto, University of Turin, Italy; Diego Calvanese, Free University of Bozen-Bolzano, Italy; Alessio Micheli, University of Pisa, Italy; Francesca A. Lisi University of Bari Aldo Moro, Italy; Mauro Vallati, University of Huddersfield, United Kingdom; Bernardo Magnini, Fondazione Bruno Kessler (FBK), Italy; Luigia C. Aiello, Sapienza University of Rome, Italy; Rafael H. Bordini, Pontifical Catholic University of Rio Grande do Sul, Brazil; Chiara Ghidini, Fondazione Bruno Kessler (FBK), Italy; Federico Bergenti, University of Parma, Italy; Francesco Ricca, University of Calabria, Italy; Andrea Burattin, Technical University of Denmark, Denmark; Angelo Oddi, ISTC-CNR, Italian National Research Council, Italy; Farshad Badie, Aalborg University, Denmark; Emanuel Sallinger, University of Oxford, United Kingdom; Marin Lujak, University Rey Juan Carlos, Spain; Giancarlo Fortino, University of Calabria, Italy; Salvatore Gaglio, University of Palermo, Italy; Johannes K. Fichte, TU Dresden, Germany; Matteo Baldoni, University of Turin, Italy; Fabrizio Riguzzi, Univeristy of Ferrara, Italy; Ivana Dusparic, Trinity College Dublin, Ireland; Serena Villata, CNRS - Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis, France; Paolo Rosso, Technical University of Valencia, Spain; Ana L. C. Bazzan, Federal University of Rio Grande do Sul, Brazil; Gianluigi Greco, University of Calabria, Italy; Stefano Cagnoni, University of Parma, Italy; Gian Luca Pozzato, University of Turin, Italy; Salvatore Ruggieri, University of Turin, Italy; Claudio Di Ciccio, Sapienza University of Rome, Italy; Eloisa Vargiu, Cetaqua, Water Technology Center, Spain; Marco Maratea, University of Genova, Italy; Andrea Roli, University of Bologna, Italy; Viviana Patti, University of Turin, Italy; Stefania Costantini, University of L'Aquila, Italy; Antonius Weinzierl, Vienna University of Technology, Austria; Silvia Rossi, University of Naples Federico II, Italy; Giovanni Semeraro, University of Bari, Italy; Luciano Serafini, Fondazione Bruno Kessler (FBK), Italy; Laura Giordano, University of Eastern Piedmont, Italy; Ernesto Jimenez-Ruiz, City, University of London, United Kingdom; Marco Gavanelli, University of Ferrara, Italy; Federico Chesani, University of Bologna, Italy

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The 20th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2021) is organized by AIxIA (Associazione Italiana per l’Intelligenza Artificiale), which is a non-profit scientific society founded in 1988 and devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.

Enhancing Telepresence Robots with AI: Combining Services to Personalize and React

In this paper we report about the relationships between a multi-preferential semantics for defeasible description logics and a deep neural network model. Weighted knowledge bases for description logics are considered under a “concept-wise" preferential semantics, which is further extended to fuzzy interpretations and exploited to provide a preferential interpretation of Multilayer Perceptrons.


Multilayer Perceptrons as Weighted Conditional Knowledge Bases: an Overview

The work introduces an elastic and platform-agnostic approach to probabilistic logic programming aimed at linking this paradigm with modern mainstream programming platforms, thus widening its usability and portability (e.g. towards the JVM, Android, Python, and JavaScript platforms). We design our solution as an extension of the 2P-Kt symbolic AI ecosystem to inherit its multi-platform and multi-paradigm nature.

Probabilistic Logic Programming in 2p-kt

Private international law (PIL) addresses overlaps and conflicts between legal systems by distributing cases between the authorities of such systems (jurisdiction) and establishing what rules these authorities have to apply to each case(choice of law). A modular argumentation tool, Arg-tuProlog, is here presented that enables reasoning with rules and interpretations of multiple legal systems.

Modular logic argumentation in Arg-tuProlog

Human Detection in Drone Images Using YOLO for Search-and-Rescue Operations

Image recognition systems have evolved so much that they can actually be exploited to solve significant challenges today, such as facilitating communication for people with hearing impairments relying on sign languages. This project aims to apply deep learning and fine-tuning techniques to build an automatic recognition system for the Italian Sign Language (LIS). More specifically, our goal is a real-time image recognition system capable of accurately identifying the letters of the LIS alphabet provided by a user in a Human Computer Interaction (HCI) framework by means of Python’s Open Source Computer Vision (OpenCV) library and two models based on convolutional neural networks, namely CNN and VGG19, applied for large-scale image and video recognition. In addition to testing the performance of different architectures, our work constitutes a novel step towards the application of automatic image recognition techniques with the recently acknowledged LIS and a lately released open-source dataset, also representing the only source available for this type of research on single-handed isolated signs. This project may not only play a role in the interpretation and learning of the Italian Sign Language, encouraging its spread and study, but also in the inclusion of hearing-impaired individuals in the language research domain.

Real-time Italian Sign Language Recognition with Deep Learning

In this paper we present ArabCeleb, a dataset collected in the wild that specifically focuses on arabic language. The proposed dataset contains utterances from 100 celebrities taken from video on YouTube.com. The dataset might be used for several speaker recognition tasks: identification, verification, gender recognition as well as multimodal recognition tasks thus integrating audio and video tracks. To complete our study, we evaluated the most recent state-of-the-art methods for speaker recognition by measuring robustness as the length of the utterances increases.

ArabCeleb: Speaker Recognition in Arabic

Static, Dynamic and Acceleration Features for CNN-based Speech Emotion Recognition

A neural-machine-translation system resilient to out of vocabulary words for translating natural language to SPARQL

This paper describes an application of textual similarity techniques in the Legal Informatics domain. In European law, a relevant interest relates to the transposition of EU directives by the Member States, which can be complete, partial, or eventually absent. As part of an European project, legal experts annotated transpositions of six directives on a per-article basis. Following an established NLP pipeline, we explore a similarity-based technique to identify correspondences between transpositions of national implementations. Early results are promising and show the role that Artificial Intelligence may play within the process of harmonization and standardization of domestic legal systems as a result of the adoption of EU legislation.

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