latest.bib

@inproceedings{AzzBelRigMAP-AIXIA-IC,
  author = {Azzolini, Damiano and Bellodi, Elena and Riguzzi, Fabrizio},
  editor = {Dovier, Agostino and Montanari, Angelo and Orlandini, Andrea},
  title = {MAP Inference in Probabilistic Answer Set Programs},
  booktitle = {AIxIA 2022 -- Advances in Artificial Intelligence},
  year = {2023},
  publisher = {Springer International Publishing},
  address = {Cham},
  pages = {413--426},
  abstract = {Reasoning with uncertain data is a central task in artificial intelligence. In some cases, the goal is to find the most likely assignment to a subset of random variables, named query variables, while some other variables are observed. This task is called Maximum a Posteriori (MAP). When the set of query variables is the complement of the observed variables, the task goes under the name of Most Probable Explanation (MPE). In this paper, we introduce the definitions of cautious and brave MAP and MPE tasks in the context of Probabilistic Answer Set Programming under the credal semantics and provide an algorithm to solve them. Empirical results show that the brave version of both tasks is usually faster to compute. On the brave MPE task, the adoption of a state-of-the-art ASP solver makes the computation much faster than a naive approach based on the enumeration of all the worlds.},
  isbn = {978-3-031-27181-6},
  url = {https://link.springer.com/chapter/10.1007/978-3-031-27181-6_29},
  doi = {10.1007/978-3-031-27181-6_29}
}

@inproceedings{AzzBelRigApprox-AIXIA-IC,
  author = {Azzolini, Damiano and Bellodi, Elena and Riguzzi, Fabrizio},
  editor = {Dovier, Agostino and Montanari, Angelo and Orlandini, Andrea},
  title = {Approximate Inference in Probabilistic Answer Set Programming for Statistical Probabilities},
  booktitle = {AIxIA 2022 -- Advances in Artificial Intelligence},
  year = {2023},
  publisher = {Springer International Publishing},
  address = {Cham},
  pages = {33--46},
  abstract = {``Type 1'' statements were introduced by Halpern in 1990 with the goal to represent statistical information about a domain of interest. These are of the form ``x{\%} of the elements share the same property''. The recently proposed language PASTA (Probabilistic Answer set programming for STAtistical probabilities) extends Probabilistic Logic Programs under the Distribution Semantics and allows the definition of this type of statements. To perform exact inference, PASTA programs are converted into probabilistic answer set programs under the Credal Semantics. However, this algorithm is infeasible for scenarios when more than a few random variables are involved. Here, we propose several algorithms to perform both conditional and unconditional approximate inference in PASTA programs and test them on different benchmarks. The results show that approximate algorithms scale to hundreds of variables and thus can manage real world domains.},
  isbn = {978-3-031-27181-6},
  url = {https://link.springer.com/chapter/10.1007/978-3-031-27181-6_3},
  doi = {10.1007/978-3-031-27181-6_3}
}

@inproceedings{AzzBelRig22-abdPASP-NC,
  title = {Abduction in (Probabilistic) Answer Set Programming},
  author = {Azzolini, Damiano and Bellodi, Elena and Riguzzi, Fabrizio},
  year = {2022},
  editor = {Roberta Calegari and Giovanni Ciatto and Andrea Omicini},
  booktitle = {Proceedings of the 36th Italian Conference on Computational Logic},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  address = {Aachen, Germany},
  issn = {1613-0073},
  venue = {Bologna, Italy},
  volume = {3204},
  pages = {90--103},
  pdf = {http://ceur-ws.org/Vol-3204/paper_9.pdf}
}

@inproceedings{AzzBellRig2022-PASTA-IC,
  author = {Azzolini, Damiano and Bellodi, Elena and Riguzzi, Fabrizio},
  editor = {Gottlob, Georg and Inclezan, Daniela and Maratea, Marco},
  title = {Statistical Statements in Probabilistic Logic Programming},
  booktitle = {Logic Programming and Nonmonotonic Reasoning},
  year = {2022},
  publisher = {Springer International Publishing},
  address = {Cham},
  pages = {43--55},
  isbn = {978-3-031-15707-3},
  doi = {10.1007/978-3-031-15707-3_4},
  url = {https://link.springer.com/chapter/10.1007/978-3-031-15707-3_4},
  pdf = {http://ml.unife.it/wp-content/uploads/Papers/AzzBellRig2022PASTA.pdf}
}

@inproceedings{AzzBelFerRigZes22-recently-IC,
  title = {Abduction in Probabilistic Logic Programs},
  booktitle = {Proceedings of the 38th International Conference on Logic Programming (Technical Communications), Recently Published Research track},
  issn = {2075-2180},
  doi = {10.4204/EPTCS.364},
  volume = {364},
  pages = {174--176},
  series = {Electronic Proceedings in Theoretical Computer Science},
  publisher = {Open Publishing Association},
  address = {Waterloo, Australia},
  editor = {Yuliya Lierler and Jose F. Morales and Carmine Dodaro and Veronica Dahl and Martin Gebser and Tuncay Tekle},
  year = {2022},
  author = {Damiano Azzolini and Elena Bellodi and Stefano Ferilli and Fabrizio Riguzzi and Riccardo Zese},
  url = {https://arxiv.org/html/2208.02685v1/#EPTCS364.27}
}

@article{AzzBellFer2022-IJAR-IJ,
  title = {Abduction with probabilistic logic programming under the distribution semantics},
  journal = {International Journal of Approximate Reasoning},
  volume = {142},
  pages = {41-63},
  year = {2022},
  issn = {0888-613X},
  doi = {10.1016/j.ijar.2021.11.003},
  url = {https://www.sciencedirect.com/science/article/pii/S0888613X2100181X},
  author = {Damiano Azzolini and Elena Bellodi and Stefano Ferilli and Fabrizio Riguzzi and Riccardo Zese},
  keywords = {Abduction, Distribution semantics, Probabilistic logic programming, Statistical relational artificial intelligence},
  abstract = {In Probabilistic Abductive Logic Programming we are given a probabilistic logic program, a set of abducible facts, and a set of constraints. Inference in probabilistic abductive logic programs aims to find a subset of the abducible facts that is compatible with the constraints and that maximizes the joint probability of the query and the constraints. In this paper, we extend the PITA reasoner with an algorithm to perform abduction on probabilistic abductive logic programs exploiting Binary Decision Diagrams. Tests on several synthetic datasets show the effectiveness of our approach.},
  scopus = {2-s2.0-85119493622}
}

@inproceedings{AzzRigBelLam22-BSCT-IW,
  title = {A Probabilistic Logic Model of Lightning Network},
  author = {Azzolini, Damiano and Riguzzi, Fabrizio and Bellodi, Elena and Lamma, Evelina},
  booktitle = {Business Information Systems Workshops},
  year = {2022},
  editor = {Abramowicz, Witold and Auer, S{\"o}ren and Str{\'o}{\.{z}}yna, Milena},
  pages = {321--333},
  series = {Lecture Notes in Business Information Processing (LNBIP)},
  publisher = {Springer International Publishing},
  address = {Cham, Switzerland},
  eventdate = {June 14-17, 2021},
  doi = {10.1007/978-3-031-04216-4_28},
  url = {https://link.springer.com/chapter/10.1007/978-3-031-04216-4_28},
  pdf = {http://ml.unife.it/wp-content/uploads/Papers/AzzRigBelLam22-BSCT-IW.pdf}
}

@inbook{ZesBelFraRigLam22-MLNVM-BC,
  author = {Zese, Riccardo and Bellodi, Elena and Fraccaroli, Michele and Riguzzi, Fabrizio and Lamma, Evelina},
  editor = {Micheloni, Rino and Zambelli, Cristian},
  title = {Neural Networks and Deep Learning Fundamentals},
  booktitle = {Machine Learning and Non-volatile Memories},
  year = {2022},
  publisher = {Springer International Publishing},
  address = {Cham},
  pages = {23--42},
  abstract = {In the last decade, Neural Networks (NNs) have come to the fore as one of the most powerful and versatile approaches to many machine learning tasks. Deep Learning (DL)Deep Learning (DL), the latest incarnation of NNs, is nowadays applied in every scenario that needs models able to predict or classify data. From computer vision to speech-to-text, DLDeep Learning (DL) techniques are able to achieve super-human performance in many cases. This chapter is devoted to give a (not comprehensive) introduction to the field, describing the main branches and model architectures, in order to try to give a roadmap of this area to the reader.},
  isbn = {978-3-031-03841-9},
  doi = {10.1007/978-3-031-03841-9_2},
  url = {https://doi.org/10.1007/978-3-031-03841-9_2}
}

@inproceedings{SerBusVen2022-ISC2-IC,
  author = {Seravalli, Alessandro and Busani, Mariaelena and Venturi, Simone and Brutti, Arianna and Petrovich, Carlo and Frascella, Angelo and Paolucci, Fabrizio and Di Felice, Marco and Lombardi, Michele and Bellodi, Elena and Zese, Riccardo and Bertasi, Francesco and Balugani, Elia and Cecaj, Alket and Gamberini, Rita and Mamei, Marco and Picone, Marco},
  booktitle = {2022 IEEE International Smart Cities Conference (ISC2)},
  title = {Towards Smart Cities for Tourism: the POLIS-EYE Project},
  year = {2022},
  volume = {},
  number = {},
  pages = {1-7},
  doi = {10.1109/ISC255366.2022.9922095}
}

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