Publications

        @incollection{ragno2024transparent, title={Transparent Explainable Logic Layers}, author={Ragno, Alessio and Plantevit, Marc and Robardet, Celine and Capobianco, Roberto}, booktitle={ECAI 2024}, pages={914--921}, year={2024}, publisher={IOS Press} }
        

        @inproceedings{UmiliEnhancing, author       = {Elena Umili and Gabriel Paludo Licks and Fabio Patrizi}, title        = {Enhancing Deep Sequence Generation with Logical Temporal Knowledge}, booktitle    = {Proceedings of the 3rd International Workshop on Process Management in the {AI} Era {(PMAI} 2024) co-located with 27th European Conference on Artificial Intelligence {(ECAI} 2024), Santiago de Compostela, Spain, October 19, 2024}, series       = {{CEUR} Workshop Proceedings}, volume       = {3779}, pages        = {23--34}, publisher    = {CEUR-WS.org}, year         = {2024}, url          = {https://ceur-ws.org/Vol-3779/paper4.pdf}, timestamp    = {Mon, 28 Oct 2024 16:46:06 +0100}, biburl       = {https://dblp.org/rec/conf/pmai/UmiliLP24.bib}, bibsource    = {dblp computer science bibliography, https://dblp.org} }
        

        @inproceedings{UmiliNeuralRewardMachines, author       = {Elena Umili and Francesco Argenziano and Roberto Capobianco}, title        = {Neural Reward Machines}, booktitle    = {{ECAI} 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain - Including 13th Conference on Prestigious Applications of Intelligent Systems {(PAIS} 2024)}, series       = {Frontiers in Artificial Intelligence and Applications}, volume       = {392}, pages        = {3055--3062}, publisher    = {{IOS} Press}, year         = {2024}, url          = {https://doi.org/10.3233/FAIA240847}, doi          = {10.3233/FAIA240847}, timestamp    = {Fri, 25 Oct 2024 12:13:46 +0200}, biburl       = {https://dblp.org/rec/conf/ecai/UmiliAC24.bib}, bibsource    = {dblp computer science bibliography, https://dblp.org} }
        

        @inproceedings{UmiliDeepDFA, author       = {Elena Umili and Roberto Capobianco}, title        = {DeepDFA: Automata Learning through Neural Probabilistic Relaxations}, booktitle    = {{ECAI} 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain - Including 13th Conference on Prestigious Applications of Intelligent Systems {(PAIS} 2024)}, series       = {Frontiers in Artificial Intelligence and Applications}, volume       = {392}, pages        = {1051--1058}, publisher    = {{IOS} Press}, year         = {2024}, url          = {https://doi.org/10.3233/FAIA240596}, doi          = {10.3233/FAIA240596}, timestamp    = {Fri, 25 Oct 2024 12:13:46 +0200}, biburl       = {https://dblp.org/rec/conf/ecai/UmiliC24.bib}, bibsource    = {dblp computer science bibliography, https://dblp.org} }
        

        @inproceedings{ LaRosa2023Towards,
 title={Towards a fuller understanding of neurons with Clustered Compositional Explanations},
 author={Biagio La Rosa and Leilani H. Gilpin and Roberto Capobianco},
 booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
 year={2023}
 }
        

        @InProceedings{Matic2023,
 author     = {Matić, Sanja Lj. and Mladenović, Tamara M. and Mladenović, Milan P. and Tomašević, Nevena M. and Capobianco, Roberto and Ragno, Alessio and Sapienza, Filippo U. and Astolfi, Roberta and Ragno, Rino},
 booktitle  = {Book of Proceedings},
 title      = {Oxidative DNA damage preventive activity of essential oils of three Pinus species: P. mugo, P. sibirica, and P. silvestre},
 year       = {2023},
 publisher  = {Institute for Information Technologies, University of Kragujevac},
 series     = {ICCBIKG 2023},
 collection = {ICCBIKG 2023},
 doi        = {10.46793/iccbi23.321m},
 }
        

        @InProceedings{Proietti2023,
 author    = {Proietti, Michela and Ragno, Alessio and Capobianco, Roberto},
 booktitle = {2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)},
 title     = {Memory Replay For Continual Learning With Spiking Neural Networks},
 year      = {2023},
 month     = sep,
 publisher = {IEEE},
 doi       = {10.1109/mlsp55844.2023.10285911},
 }
        

        @Article{LaRosa2023,
 author    = {{La Rosa}, B. and Blasilli, G. and Bourqui, R. and Auber, D. and Santucci, G. and Capobianco, R. and Bertini, E. and Giot, R. and Angelini, M.}, journal   = {Computer Graphics Forum},
 title     = {State of the Art of Visual Analytics for eXplainable Deep Learning},
 year      = {2023},
 issn      = {1467-8659},
 month     = feb,
 number    = {1},
 pages     = {319--355},
 volume    = {42},
 doi       = {10.1111/cgf.14733},
 publisher = {Wiley},
 }
        

        @Article{Proietti2023,
 author    = {Michela Proietti and Alessio Ragno and Biagio La Rosa and Rino Ragno and Roberto Capobianco},
 journal   = {Machine Learning},
 title     = {Explainable {AI} in drug discovery: self-interpretable graph neural network for molecular property prediction using concept whitening},
 year      = {2023},
 month     = {oct},
 doi       = {10.1007/s10994-023-06369-y},
 publisher = {Springer Science and Business Media {LLC}},
 }
        

        @inproceedings{Umili2023Grounding,
 title     = {{Grounding LTLf Specifications in Image Sequences}},
 author    = {Umili, Elena and Capobianco, Roberto and De Giacomo, Giuseppe},
 booktitle = {{Proceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning}},
 pages     = {668--678},
 year      = {2023},
 month     = {8},
 doi       = {10.24963/kr.2023/65},
 url       = {https://doi.org/10.24963/kr.2023/65},
 }
        

        @InProceedings{Umili2023Neurosymbolic,
 author={Umili, Elena},
 editor={Malvone, Vadim and Murano, Aniello},
 title={Neurosymbolic Integration of Linear Temporal Logic in Non Symbolic Domains},
 booktitle={Multi-Agent Systems},
 year={2023},
 publisher={Springer Nature Switzerland},
 address={Cham},
 pages={521--527},
 isbn={978-3-031-43264-4} }
        

        @InProceedings{Ciabatti2023,
 author={Ciabatti, Giulia and Spiller, Dario and Daftry, Shreyansh and Capobianco, Roberto and Curti, Fabio}, editor={Ieracitano, Cosimo and Mammone, Nadia and Di Clemente, Marco and Mahmud, Mufti and Furfaro, Roberto and Morabito, Francesco Carlo},
 title={Deep Reinforcement Learning for Pin-Point Autonomous Lunar Landing: Trajectory Recalculation for Obstacle Avoidance},
 booktitle={The Use of Artificial Intelligence for Space Applications},
 year={2023},
 publisher={Springer Nature Switzerland},
 address={Cham},
 pages={101--115},
 isbn={978-3-031-25755-1} } 
        

        @inproceedings{DBLP:conf/nesy/UmiliABC23,
 author       = {Elena Umili and Francesco Argenziano and Aymeric Barbin and Roberto Capobianco},
 title        = {Visual Reward Machines},
 booktitle    = {Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning, La Certosa di Pontignano, Siena, Italy, July 3-5, 2023},
 pages        = {255--267},
 year         = {2023},
 crossref     = {DBLP:conf/nesy/2023},
 url          = {https://ceur-ws.org/Vol-3432/paper23.pdf},
 timestamp    = {Tue, 11 Jul 2023 17:14:10 +0200},
 biburl       = {https://dblp.org/rec/conf/nesy/UmiliABC23.bib},
 bibsource    = {dblp computer science bibliography, https://dblp.org} } 
        

        @Article{Ocana2023,
 author    = {Jim Martin Catacora Ocana and Roberto Capobianco and Daniele Nardi},
 journal   = {Journal of Artificial Intelligence Research},
 title     = {An Overview of Environmental Features that Impact Deep Reinforcement Learning in Sparse-Reward Domains},
 year      = {2023},
 month     = {apr},
 pages     = {1181--1218},
 volume    = {76},
 doi       = {10.1613/jair.1.14390},
 publisher = {{AI} Access Foundation},
 } 
        

        @inproceedings{Latorre2022, author = {Latorre, Francesco and Carbone, Andrea and Thottuchirayil Sasidharan, Sarathchandrakumar and Ciabatti, Giulia and Spiller, Dario and Curti, Fabio and Capobianco, Roberto}, year = {2022}, month = {09}, pages = {}, title = {A MOON OPTICAL NAVIGATION ROBOTIC FACILITY ON SIMULATED TERRAIN: MONSTER} } 
        

        @InProceedings{Singh2022,
 author    = {Shrijit Singh and Shreyansh Daftry and Roberto Capobianco},
 booktitle = {{AIAA} {SCITECH} 2022 Forum},
 title     = {Planetary Environment Prediction Using Generative Modeling},
 year      = {2022},
 month     = {jan},
 publisher = {American Institute of Aeronautics and Astronautics},
 doi       = {10.2514/6.2022-2085},
 } 
        

        @Article{Wurman2022,
 author    = {Peter R. Wurman and Samuel Barrett and Kenta Kawamoto and James MacGlashan and Kaushik Subramanian and Thomas J. Walsh and Roberto Capobianco and Alisa Devlic and Franziska Eckert and Florian Fuchs and Leilani Gilpin and Piyush Khandelwal and Varun Kompella and HaoChih Lin and Patrick MacAlpine and Declan Oller and Takuma Seno and Craig Sherstan and Michael D. Thomure and Houmehr Aghabozorgi and Leon Barrett and Rory Douglas and Dion Whitehead and Peter Dürr and Peter Stone and Michael Spranger and Hiroaki Kitano},
 journal   = {Nature},
 title     = {Outracing champion Gran Turismo drivers with deep reinforcement learning},
 year      = {2022},
 month     = {feb},
 number    = {7896},
 pages     = {223--228},
 volume    = {602},
 doi       = {10.1038/s41586-021-04357-7},
 publisher = {Springer Science and Business Media {LLC}}, } 
        

        @Article{Proia2022,
 author    = {Eleonora Proia and Alessio Ragno and Lorenzo Antonini and Manuela Sabatino and Milan Mladenovi{{c}} and Roberto Capobianco and Rino Ragno},
 journal   = {Journal of Computer-Aided Molecular Design},
 title     = {Ligand-based and structure-based studies to develop predictive models for {SARS}-{CoV}-2 main protease inhibitors through the 3d-qsar.com portal},
 year      = {2022},
 month     = {jun},
 number    = {7},
 pages     = {483--505},
 volume    = {36},
 doi       = {10.1007/s10822-022-00460-7},
 publisher = {Springer Science and Business Media {LLC}}, }
        

        @Article{Ragno2022,
 author={Ragno, Alessio and La Rosa, Biagio and Capobianco, Roberto},
 journal={IEEE Transactions on Artificial Intelligence}, 
 title={Prototype-based Interpretable Graph Neural Networks}, 
 year={2022},
 volume={},
 number={},
 pages={1-11},
 doi={10.1109/TAI.2022.3222618}
 }
        

        @inproceedings{umili2022,
 author = {Elena Umili and Roberto Capobianco and Giuseppe {De Giacomo}},
 title = {Grounding LTLf Specifications in Images},
 booktitle = {16th International Workshop on Neural-Symbolic Learning and Reasoning},
 year = {2022}, }
        

        @Article{LaRosa2022,
 author    = {Biagio La Rosa and Roberto Capobianco and Daniele Nardi},
 journal   = {Applied Intelligence},
 title     = {A self-interpretable module for deep image classification on small data},
 year      = {2022},
 month     = {aug},
 doi       = {10.1007/s10489-022-03886-6},
 publisher = {Springer Science and Business Media {LLC}},
 }
        

        @Article{Savoia2022,
 author        = {Dylan Savoia and Alessio Ragno and Roberto Capobianco},
 journal       = {ELLIS Machine Learning for Molecules workshop (ML4Molecules) 2021},
 title         = {Molecule Generation from Input-Attributions over Graph Convolutional Networks},
 year          = {2022},
 month         = jan,
 abstract      = {It is well known that Drug Design is often a costly process both in terms of time and economic effort. While good Quantitative Structure-Activity Relationship models (QSAR) can help predicting molecular properties without the need to synthesize them, it is still required to come up with new molecules to be tested. This is mostly done in lack of tools to determine which modifications are more promising or which aspects of a molecule are more influential for the final activity/property. Here we present an automatic process which involves Graph Convolutional Network models and input-attribution methods to generate new molecules. We also explore the problems of over-optimization and applicability, recognizing them as two important aspects in the practical use of such automatic tools.},
 archiveprefix = {arXiv},
 eprint        = {2202.05703},
 keywords      = {q-bio.BM, cs.AI, cs.LG},
 primaryclass  = {q-bio.BM},
 }
        

        @Article{Ragno2022,
 author        = {Alessio Ragno and Dylan Savoia and Roberto Capobianco},
 journal       = {ELLIS Machine Learning for Molecules workshop (ML4Molecules) 2021},
 title         = {Semi-Supervised GCN for learning Molecular Structure-Activity Relationships},
 year          = {2022},
 month         = jan,
 abstract      = {Since the introduction of artificial intelligence in medicinal chemistry, the necessity has emerged to analyse how molecular property variation is modulated by either single atoms or chemical groups. In this paper, we propose to train graph-to-graph neural network using semi-supervised learning for attributing structure-property relationships. As initial case studies we apply the method to solubility and molecular acidity while checking its consistency in comparison with known experimental chemical data. As final goal, our approach could represent a valuable tool to deal with problems such as activity cliffs, lead optimization and de-novo drug design.},
 archiveprefix = {arXiv},
 eprint        = {2202.05704},
 keywords      = {q-bio.BM, cs.AI, cs.LG},
 primaryclass  = {q-bio.BM},
 }
        

        @InProceedings{Ocana2022,
 author={Ocana, Jim Martin Catacora and Capobianco, Roberto and Nardi, Daniele},
 editor={Bandini, Stefania and Gasparini, Francesca and Mascardi, Viviana and Palmonari, Matteo and Vizzari, Giuseppe},
 title={Exploration-Intensive Distractors: Two Environment Proposals and a Benchmarking},
 booktitle={AIxIA 2021 -- Advances in Artificial Intelligence},
 year={2022},
 publisher={Springer International Publishing},
 address={Cham},
 pages={413--428},
 isbn={978-3-031-08421-8},
 }
        

        @InProceedings{Gallotta2022,
 author={Gallotta, Roberto and Capobianco, Roberto}, 
 editor={Bandini, Stefania and Gasparini, Francesca and Mascardi, Viviana and Palmonari, Matteo and Vizzari, Giuseppe},
 title={Tafl-ES: Exploring Evolution Strategies for Asymmetrical Board Games},
 booktitle={AIxIA 2021 -- Advances in Artificial Intelligence},
 year={2022},
 publisher={Springer International Publishing},
 address={Cham},
 pages={46--58}, isbn={978-3-031-08421-8},
 }
        

        @InProceedings{Makinwa2022,
 author={Makinwa, Sayo M. and La Rosa, Biagio and Capobianco, Roberto},
 editor={Bandini, Stefania and Gasparini, Francesca and Mascardi, Viviana and Palmonari, Matteo and Vizzari, Giuseppe},
 title={Detection Accuracy for Evaluating Compositional Explanations of Units},
 booktitle={AIxIA 2021 -- Advances in Artificial Intelligence},
 year={2022},
 publisher={Springer International Publishing},
 address={Cham},
 pages={550--563},
 isbn={978-3-031-08421-8},
 }
        

        @inproceedings{umili2021,
 author = {Elena Umili and Emanuele Antonioni and Francesco Riccio and
 Roberto Capobianco and Daniele Nardi and Giuseppe {De Giacomo}},
 title = {Learning a Symbolic Planning Domain through the Interaction with Continuous Environments},
 booktitle = {Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning ({PRL})},
 year = {2021},
 }
        

        @inproceedings{LaRosa2021,
 title     = {A Discussion about Explainable Inference on Sequential Data via Memory-Tracking},
 author    = {La Rosa, Biagio and Capobianco, Roberto and Nardi, Daniele}, booktitle = {AIxIA 2021 Discussion Papers},
 publisher = {CEUR Workshop Proceedings},  
 volume     = {3078},
 pages     = {33-44},
 year      = {2021},
 url       = {http://ceur-ws.org/Vol-3078/paper-24.pdf}, }
        

        @inproceedings{capobianco2021covid,
 title={Agent-Based {M}arkov Modeling for Improved {COVID}-19 Mitigation Policies},
 author={Roberto Capobianco and Varun Kompella and James Ault and Guni Sharon and Stacy Jong and Spencer Fox and Lauren Meyers and Peter R. Wurman and Peter Stone},
 month = {August},
 year={2021},
 pages = {953--992},
 Journal = {Journal of Artificial Intelligence Research (JAIR)},
 Volume = {71},
 Publisher = {Association for the Advancement of Artificial Intelligence}
 }
        

        @InProceedings{Ciabatti2021, author    = {Giulia Ciabatti and Shreyansh Daftry and Roberto Capobianco}, booktitle = {{ASCEND} 2021}, title     = {Learning Transferable Policies for Autonomous Planetary Landing via Deep Reinforcement Learning}, year      = {2021}, month     = {nov}, publisher = {American Institute of Aeronautics and Astronautics}, doi       = {10.2514/6.2021-4006}, }
        

        @InProceedings{Ciabatti_2021_CVPR,
 author = {Ciabatti, Giulia and Daftry, Shreyansh and Capobianco, Roberto},
 title = {Autonomous Planetary Landing via Deep Reinforcement Learning and Transfer Learning}, 
 booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
 month = {June},
 year = {2021}, 
 pages = {2031-2038} }
        

        @inproceedings{sharon2021covid,
 title={Multiagent Epidemiologic Inference through Realtime Contact Tracing},
 author={Sharon, Guni and Ault, James and Stone, Peter and Kompella, Varun and Capobianco, Roberto},
 booktitle={Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2021)},
 location = {London, UK},
 month = {May},
 year={2021},
 organization={International Foundation for Autonomous Agents and Multiagent Systems}
 }
        

        @article{RICCIO2021103693,
 title = {LoOP: Iterative learning for optimistic planning on robots},
 journal = {Robotics and Autonomous Systems},
 abstract = {Efficient robotic behaviors require robustness and adaptation to dynamic changes of the environment, whose characteristics rapidly vary during robot operation. To generate effective robot action policies, planning and learning techniques have shown the most promising results. However, if considered individually, they present different limitations. Planning techniques lack generalization among similar states and require experts to define behavioral routines at different levels of abstraction. Conversely, learning methods usually require a considerable number of training samples and iterations of the algorithm. To overcome these issues, and to efficiently generate robot behaviors, we introduce LoOP, an iterative learning algorithm for optimistic planning that combines state-of-the-art planning and learning techniques to generate action policies. The main contribution of LoOP is the combination of Monte-Carlo Search Planning and Q-learning, which enables focused exploration during policy refinement in different robotic applications. We demonstrate the robustness and flexibility of LoOP in various domains and multiple robotic platforms, by validating the proposed approach with an extensive experimental evaluation.}
 volume = {136},
 pages = {103693},
 year = {2021},
 issn = {0921-8890},
 doi = {https://doi.org/10.1016/j.robot.2020.103693},
 url = {http://www.sciencedirect.com/science/article/pii/S0921889020305339},
 author = {Francesco Riccio and Roberto Capobianco and Daniele Nardi},
 keywords = {Autonomous planning and learning, Monte-Carlo planning, Q-learning, Deep robot reinforcement learning},
 }
        

        @misc{kompella2020reinforcement,
 title={Reinforcement Learning for Optimization of COVID-19 Mitigation policies}, 
 author={Varun Kompella and Roberto Capobianco and Stacy Jong and Jonathan Browne and Spencer Fox and Lauren Meyers and Peter Wurman and Peter Stone},
 year={2020},
 eprint={2010.10560},
 archivePrefix={arXiv},
 primaryClass={cs.LG} }
        

        @inproceedings{10.5555/3398761.3399047,
 author = {Riccio, Francesco and Capobianco, Roberto and Nardi, Daniele},
 title = {GUESs: Generative Modeling of Unknown Environments and Spatial Abstraction for Robots},
 year = {2020},
 isbn = {9781450375184},
 publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
 address = {Richland, SC},
 abstract = {Representing unknown and missing knowledge about the environment is fundamental to leverage robot behavior and improve its performance in completing a task. However, reconstructing spatial knowledge beyond the sensory horizon of the robot is an extremely challenging task. Existing approaches assume that the environment static and features repetitive patterns (e.g. rectangular rooms) or that it can be all generalized with pre-trained models. Our goal is to remove such assumptions and to introduce a novel methodology that allows the robot to represent unknown spatial knowledge in dynamic and unstructured environments. To this end, we exploit generative learning to (1) learn a distribution of spatial landmarks observed during the robot mission and to (2) generate missing information in real-time. The proposed approach aims at supporting planning and decision-making processes needed for robot behaviors. In this paper, we describe architecture modeling the proposed approach and a first validation on a mobile platform.}, booktitle = {Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems},
 pages = {1978–1980},
 numpages = {3},
 keywords = {generative learning, knowledge representation, robot learning},
 location = {Auckland, New Zealand},
 series = {AAMAS '20} }
        

        @inproceedings{LaRosa20,
 title     = {Explainable Inference on Sequential Data via Memory-Tracking},
 author    = {La Rosa, Biagio and Capobianco, Roberto and Nardi, Daniele},
 booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}},
 publisher = {International Joint Conferences on Artificial Intelligence Organization},  
 pages     = {2006--2013},
 year      = {2020},
 month     = {7},
 doi       = {10.24963/ijcai.2020/278},
 url       = {https://doi.org/10.24963/ijcai.2020/278},
 }