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1. Integrating Classical Planners with GPT-Based Planning Policies

Author

Tummolo, Massimiliano, Rossetti, Nicholas, Gerevini, Alfonso Emilio, Olivato, Matteo, Putelli, Luca, Serina, Ivan, 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. Goal Recognition as a Deep Learning Task: the GRNet Approach

Author

Chiari, Mattia, Gerevini, Alfonso E., Putelli, Luca, Percassi, Francesco, and Serina, Ivan

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

In automated planning, recognising the goal of an agent from a trace of observations is an important task with many applications. The state-of-the-art approaches to goal recognition rely on the application of planning techniques, which requires a model of the domain actions and of the initial domain state (written, e.g., in PDDL). We study an alternative approach where goal recognition is formulated as a classification task addressed by machine learning. Our approach, called GRNet, is primarily aimed at making goal recognition more accurate as well as faster by learning how to solve it in a given domain. Given a planning domain specified by a set of propositions and a set of action names, the goal classification instances in the domain are solved by a Recurrent Neural Network (RNN). A run of the RNN processes a trace of observed actions to compute how likely it is that each domain proposition is part of the agent's goal, for the problem instance under considerations. These predictions are then aggregated to choose one of the candidate goals. The only information required as input of the trained RNN is a trace of action labels, each one indicating just the name of an observed action. An experimental analysis confirms that \our achieves good performance in terms of both goal classification accuracy and runtime, obtaining better performance w.r.t. a state-of-the-art goal recognition system over the considered benchmarks.

Published
2022

3. Enhancing GPT-Based Planning Policies by Model-Based Plan Validation

Author

Rossetti, Nicholas, Tummolo, Massimiliano, Gerevini, Alfonso Emilio, Olivato, Matteo, Putelli, Luca, Serina, Ivan, 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, Besold, Tarek R., editor, d’Avila Garcez, Artur, editor, Jimenez-Ruiz, Ernesto, editor, Confalonieri, Roberto, editor, Madhyastha, Pranava, editor, and Wagner, Benedikt, editor

Published
2024
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4. Action-Failure Resilient Planning

Author

Aineto, Diego, primary, Gaudenzi, Alessandro, additional, Gerevini, Alfonso, additional, Rovetta, Alberto, additional, Scala, Enrico, additional, and Serina, Ivan, additional

Published
2023
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5. Prognosis Prediction in Covid-19 Patients from Lab Tests and X-ray Data through Randomized Decision Trees

Author

Gerevini, Alfonso Emilio, Maroldi, Roberto, Olivato, Matteo, Putelli, Luca, and Serina, Ivan

Subjects
Computer Science - Machine Learning
Abstract

AI and Machine Learning can offer powerful tools to help in the fight against Covid-19. In this paper we present a study and a concrete tool based on machine learning to predict the prognosis of hospitalised patients with Covid-19. In particular we address the task of predicting the risk of death of a patient at different times of the hospitalisation, on the base of some demographic information, chest X-ray scores and several laboratory findings. Our machine learning models use ensembles of decision trees trained and tested using data from more than 2000 patients. An experimental evaluation of the models shows good performance in solving the addressed task., Comment: 5th International Workshop on Knowledge Discovery in Healthcare Data (KDH) at ECAI 2020, mortality prediction, COVID-19

Published
2020

6. A BERT-Based Scoring System for Workplace Safety Courses in Italian

Author

Arici, Nicola, Gerevini, Alfonso E., Putelli, Luca, Serina, Ivan, Sigalini, Luca, 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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7. Novelty Messages Filtering for Multi Agent Privacy-preserving Planning

Author

Gerevini, Alfonso E., Lipovetzky, Nir, Peli, Nico, Percassi, Francesco, Saetti, Alessandro, and Serina, Ivan

Subjects
Computer Science - Artificial Intelligence
Abstract

In multi-agent planning, agents jointly compute a plan that achieves mutual goals, keeping certain information private to the individual agents. Agents' coordination is achieved through the transmission of messages. These messages can be a source of privacy leakage as they can permit a malicious agent to collect information about other agents' actions and search states. In this paper, we investigate the usage of novelty techniques in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents' privacy and performance. In particular, we show that the use of novelty based techniques can significantly reduce the number of messages transmitted among agents, better preserving their privacy and improving their performance. An experimental study analyses the effectiveness of our techniques and compares them with the state-of-the-art. Finally, we evaluate the robustness of our approach, considering different delays in the transmission of messages as they would occur in overloaded networks, due for example to massive attacks or critical situations., Comment: Accepted in SOCS-19. arXiv admin note: text overlap with arXiv:1706.06927 by other authors and arXiv:1906.03955

Published
2019

8. Best-First Width Search for Multi Agent Privacy-preserving Planning

Author

Gerevini, Alfonso E., Lipovetzky, Nir, Percassi, Francesco, Saetti, Alessandro, and Serina, Ivan

Subjects
Computer Science - Artificial Intelligence
Abstract

In multi-agent planning, preserving the agents' privacy has become an increasingly popular research topic. For preserving the agents' privacy, agents jointly compute a plan that achieves mutual goals by keeping certain information private to the individual agents. Unfortunately, this can severely restrict the accuracy of the heuristic functions used while searching for solutions. It has been recently shown that, for centralized planning, the performance of goal oriented search can be improved by combining goal oriented search and width-based search. The combination of these techniques has been called best-first width search. In this paper, we investigate the usage of best-first width search in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents' privacy and performance. In particular, we show that best-first width search is a very effective approach over several benchmark domains, even when the search is driven by heuristics that roughly estimate the distance from goal states, computed without using the private information of other agents. An experimental study analyses the effectiveness of our techniques and compares them with the state-of-the-art., Comment: Accepted in ICAPS-19

Published
2019

12. Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area.

Author

Tariku, Girma, Ghiglieno, Isabella, Sanchez Morchio, Andres, Facciano, Luca, Birolleau, Celine, Simonetto, Anna, Serina, Ivan, and Gilioli, Gianni

Subjects
LAND cover, REMOTE-sensing images, AGRICULTURE, ARTIFICIAL intelligence, REMOTE sensing, DEEP learning
Abstract

Land cover mapping is essential to understanding global land-use patterns and studying biodiversity composition and the functioning of eco-systems. The introduction of remote sensing technologies and artificial intelligence models made it possible to base land cover mapping on satellite imagery in order to monitor changes, assess ecosystem health, support conservation efforts, and reduce monitoring time. However, significant challenges remain in managing large, complex satellite imagery datasets, acquiring specialized datasets due to high costs and labor intensity, including a lack of comparative studies for the selection of optimal deep learning models. No less important is the scarcity of aerial datasets specifically tailored for agricultural areas. This study addresses these gaps by presenting a methodology for semantic segmentation of land covers in agricultural areas using satellite images and deep learning models with pre-trained backbones. We introduce an efficient methodology for preparing semantic segmentation datasets and contribute the "Land Cover Aerial Imagery" (LICAI) dataset for semantic segmentation. The study focuses on the Franciacorta area, Lombardy Region, leveraging the rich diversity of the dataset to effectively train and evaluate the models. We conducted a comparative study, using cutting-edge deep-learning-based segmentation models (U-Net, SegNet, DeepLabV3) with various pre-trained backbones (ResNet, Inception, DenseNet, EfficientNet) on our dataset acquired from Google Earth Pro. Through meticulous data acquisition, preprocessing, model selection, and evaluation, we demonstrate the effectiveness of these techniques in accurately identifying land cover classes. Integrating pre-trained feature extraction networks significantly improves performance across various metrics. Additionally, addressing challenges such as data availability, computational resources, and model interpretability is essential for advancing the field of remote sensing, in support of biodiversity conservation and the provision of ecosystem services and sustainable agriculture. [ABSTRACT FROM AUTHOR]

Published
2025
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16. An Application of Recurrent Neural Networks for Estimating the Prognosis of COVID-19 Patients in Northern Italy

Author

Chiari, Mattia, Gerevini, Alfonso E., Olivato, Matteo, Putelli, Luca, Rossetti, Nicholas, Serina, Ivan, 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, Tucker, Allan, editor, Henriques Abreu, Pedro, editor, Cardoso, Jaime, editor, Pereira Rodrigues, Pedro, editor, and Riaño, David, editor

Published
2021
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17. Attention-Based Explanation in a Deep Learning Model For Classifying Radiology Reports

Author

Putelli, Luca, Gerevini, Alfonso E., Lavelli, Alberto, Maroldi, Roberto, Serina, Ivan, 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, Tucker, Allan, editor, Henriques Abreu, Pedro, editor, Cardoso, Jaime, editor, Pereira Rodrigues, Pedro, editor, and Riaño, David, editor

Published
2021
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18. Length of Stay Prediction for Northern Italy COVID-19 Patients Based on Lab Tests and X-Ray Data

Author

Chiari, Mattia, Gerevini, Alfonso E., Maroldi, Roberto, Olivato, Matteo, Putelli, Luca, Serina, Ivan, 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, Del Bimbo, Alberto, editor, Cucchiara, Rita, editor, Sclaroff, Stan, editor, Farinella, Giovanni Maria, editor, Mei, Tao, editor, Bertini, Marco, editor, Escalante, Hugo Jair, editor, and Vezzani, Roberto, editor

Published
2021
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24. Leveraging Multi-task Learning for Biomedical Named Entity Recognition

Author

Mehmood, Tahir, Gerevini, Alfonso, Lavelli, Alberto, Serina, Ivan, 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, Alviano, Mario, editor, Greco, Gianluigi, editor, and Scarcello, Francesco, editor

Published
2019
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25. Applying Self-interaction Attention for Extracting Drug-Drug Interactions

Author

Putelli, Luca, Gerevini, Alfonso E., Lavelli, Alberto, Serina, Ivan, 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, Alviano, Mario, editor, Greco, Gianluigi, editor, and Scarcello, Francesco, editor

Published
2019
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26. Case-Based Merging Techniques in OAKPLAN

Author

Roubíčková, Anna and Serina, Ivan

Subjects
Computer Science - Artificial Intelligence
Abstract

Case-based planning can take advantage of former problem-solving experiences by storing in a plan library previously generated plans that can be reused to solve similar planning problems in the future. Although comparative worst-case complexity analyses of plan generation and reuse techniques reveal that it is not possible to achieve provable efficiency gain of reuse over generation, we show that the case-based planning approach can be an effective alternative to plan generation when similar reuse candidates can be chosen., Comment: preliminary version

Published
2013

27. Planning with Partial Preference Models

Author

Nguyen, Tuan, Do, Minh, Gerevini, Alfonso, Serina, Ivan, Srivastava, Biplav, and Kambhampati, Subbarao

Subjects
Computer Science - Artificial Intelligence
Abstract

Current work in planning with preferences assume that the user's preference models are completely specified and aim to search for a single solution plan. In many real-world planning scenarios, however, the user probably cannot provide any information about her desired plans, or in some cases can only express partial preferences. In such situations, the planner has to present not only one but a set of plans to the user, with the hope that some of them are similar to the plan she prefers. We first propose the usage of different measures to capture quality of plan sets that are suitable for such scenarios: domain-independent distance measures defined based on plan elements (actions, states, causal links) if no knowledge of the user's preferences is given, and the Integrated Convex Preference measure in case the user's partial preference is provided. We then investigate various heuristic approaches to find set of plans according to these measures, and present empirical results demonstrating the promise of our approach., Comment: 38 pages, submitted to Artificial Intelligence Journal

Published
2011

32. Iterative Width Search for Multi Agent Privacy-Preserving Planning

Author

Bazzotti, Gabriele, Gerevini, Alfonso Emilio, Lipovetzky, Nir, Percassi, Francesco, Saetti, Alessandro, Serina, Ivan, 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, Ghidini, Chiara, editor, Magnini, Bernardo, editor, Passerini, Andrea, editor, and Traverso, Paolo, editor

Published
2018
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34. On the Evolution of Planner-Specific Macro Sets

Author

Vallati, Mauro, Chrpa, Lukáš, Serina, Ivan, 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, Esposito, Floriana, editor, Basili, Roberto, editor, Ferilli, Stefano, editor, and Lisi, Francesca A., editor

Published
2017
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35. Automatic Classification of Radiological Reports for Clinical Care

Author

Gerevini, Alfonso E., Lavelli, Alberto, Maffi, Alessandro, Maroldi, Roberto, Minard, Anne-Lyse, Serina, Ivan, Squassina, Guido, 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, ten Teije, Annette, editor, Popow, Christian, editor, Holmes, John H., editor, and Sacchi, Lucia, editor

Published
2017
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43. On the Plan-Library Maintenance Problem in a Case-Based Planner

Author

Gerevini, Alfonso Emilio, Roubíčková, Anna, Saetti, Alessandro, Serina, Ivan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Delany, Sarah Jane, editor, and Ontañón, Santiago, editor

Published
2013
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44. Offline and Online Plan Library Maintenance in Case-Based Planning

Author

Gerevini, Alfonso E., Roubíčková, Anna, Saetti, Alessandro, Serina, Ivan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Baldoni, Matteo, editor, Baroglio, Cristina, editor, Boella, Guido, editor, and Micalizio, Roberto, editor

Published
2013
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45. Planning and Execution in a Personalised E-Learning Setting

Author

Morales, Lluvia, Garrido, Antonio, Serina, Ivan, 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, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Lozano, Jose A., editor, Gámez, José A., editor, and Moreno, José A., editor

Published
2011
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