@inproceedings{GenBizAzz23-ILP-IC, author = {Gentili, Elisabetta and Bizzarri, Alice and Azzolini, Damiano and Zese, Riccardo and Riguzzi, Fabrizio}, editor = {Bellodi, Elena and Lisi, Francesca Alessandra and Zese, Riccardo}, title = {Regularization in Probabilistic Inductive Logic Programming}, booktitle = {Inductive Logic Programming - ILP 2023}, year = {2023}, publisher = {Springer Nature Switzerland}, address = {Cham}, pages = {16--29}, isbn = {978-3-031-49299-0}, doi = {10.1007/978-3-031-49299-0_2}, series = {Lecture Notes in Artificial Intelligence}, volume = {14363}, url = {https://link.springer.com/chapter/10.1007/978-3-031-49299-0_2} }

@inproceedings{Azz23-ILP-IC, author = {Azzolini, Damiano}, editor = {Bellodi, Elena and Lisi, Francesca Alessandra and Zese, Riccardo}, title = {A Constrained Optimization Approach to Set the Parameters of Probabilistic Answer Set Programs}, booktitle = {Inductive Logic Programming}, year = {2023}, publisher = {Springer Nature Switzerland}, address = {Cham}, pages = {1--15}, isbn = {978-3-031-49299-0}, doi = {10.1007/978-3-031-49299-0_1}, url = {https://link.springer.com/chapter/10.1007/978-3-031-49299-0_1} }

@inproceedings{Azz2023Pasta-IC, author = {Azzolini, Damiano}, title = {On the Development of {PASTA}: Inference in Probabilistic Answer Set Programming under the Credal Semantics}, year = {2023}, journal = {Electronic Proceedings in Theoretical Computer Science, EPTCS}, volume = {385}, pages = {314 -- 315}, doi = {10.4204/EPTCS.385.30}, url = {https://cgi.cse.unsw.edu.au/~eptcs/content.cgi?ICLP2023#EPTCS385.30} }

@inproceedings{AzzBelRig2023-summary-statements-IC, author = {Azzolini, Damiano and Bellodi, Elena and Riguzzi, Fabrizio}, title = {Summary of Statistical Statements in Probabilistic Logic Programming}, year = {2023}, journal = {Electronic Proceedings in Theoretical Computer Science, EPTCS}, volume = {385}, pages = {384 -- 385}, doi = {10.4204/EPTCS.385.41}, url = {https://cgi.cse.unsw.edu.au/~eptcs/content.cgi?ICLP2023#EPTCS385.41} }

@inproceedings{AzzBelRig2023-towardsdt-IC, author = {Azzolini, Damiano and Bellodi, Elena and Riguzzi, Fabrizio}, title = {Towards a Representation of Decision Theory Problems with Probabilistic Answer Set Programs}, year = {2023}, journal = {Electronic Proceedings in Theoretical Computer Science, EPTCS}, volume = {385}, pages = {190 -- 191}, doi = {10.4204/EPTCS.385.19}, url = {https://cgi.cse.unsw.edu.au/~eptcs/content.cgi?ICLP2023#EPTCS385.19} }

@inproceedings{AzzRig2023-AIXIA-IC, title = {Inference in Probabilistic Answer Set Programming under the Credal Semantics}, author = {Damiano Azzolini and Fabrizio Riguzzi}, booktitle = {AIxIA 2023 - Advances in Artificial Intelligence}, year = {2023}, editor = {Roberto Basili and Domenico Lembo and Carla Limongelli and Andrea Orlandini}, publisher = {Springer}, volume = {14318}, address = {Heidelberg, Germany}, series = {Lecture Notes in Artificial Intelligence}, venue = {Roma, Italy}, eventdate = {November 6--9, 2023}, doi = {10.1007/978-3-031-47546-7_25}, url = {https://link.springer.com/chapter/10.1007/978-3-031-47546-7_25}, pages = {367-380} }

@article{AzzRig23-IJAR-IJ, title = {Lifted Inference for Statistical Statements in Probabilistic Answer Set Programming}, author = {Damiano Azzolini and Fabrizio Riguzzi}, journal = {International Journal of Approximate Reasoning}, year = {2023}, doi = {10.1016/j.ijar.2023.109040}, pages = {109040}, volume = {163}, issn = {0888-613X}, url = {https://www.sciencedirect.com/science/article/pii/S0888613X23001718}, keywords = {Statistical statements, Probabilistic answer set programming, Lifted inference}, abstract = {In 1990, Halpern proposed the distinction between Type 1 and Type 2 statements: the former express statistical information about a domain of interest while the latter define a degree of belief. An example of Type 1 statement is “30% of the elements of a domain share the same property” while an example of Type 2 statement is “the element x has the property y with probability p”. Recently, Type 1 statements were given an interpretation in terms of probabilistic answer set programs under the credal semantics in the PASTA framework. The algorithm proposed for inference requires the enumeration of all the answer sets of a given program, and so it is impractical for domains of not trivial size. The field of lifted inference aims to identify programs where inference can be computed without grounding the program. In this paper, we identify some classes of PASTA programs for which we apply lifted inference and develop compact formulas to compute the probability bounds of a query without the need to generate all the possible answer sets.}, scopus = {2-s2.0-85174067981} }

@inproceedings{AzzGenRigPLP2023-IW, author = {Azzolini, Damiano and Gentili, Elisabetta and Riguzzi, Fabrizio}, title = {Link Prediction in Knowledge Graphs with Probabilistic Logic Programming: Work in Progress}, series = {{CEUR} Workshop Proceedings}, booktitle = {Proceedings of the International Conference on Logic Programming 2023 Workshops co-located with the 39th International Conference on Logic Programming ({ICLP} 2023)}, editor = {Arias, Joaquín and Batsakis, Sotiris and Faber, Wolfgang and Gupta, Gopal and Pacenza, Francesco and Papadakis, Emmanuel and Robaldo, Livio and Ruckschloss, Kilian and Salazar, Elmer and Saribatur, Zeynep G. and Tachmazidis, Ilias and Weitkamper, Felix and Wyner, Adam}, volume = {3437}, pages = {1--4}, publisher = {CEUR-WS.org}, year = {2023}, url = {https://ceur-ws.org/Vol-3437/short5PLP.pdf} }

@inproceedings{AzzPLP2023-IW, author = {Azzolini, Damiano}, title = {A Brief Discussion about the Credal Semantics for Probabilistic Answer Set Programs}, series = {{CEUR} Workshop Proceedings}, booktitle = {Proceedings of the International Conference on Logic Programming 2023 Workshops co-located with the 39th International Conference on Logic Programming ({ICLP} 2023)}, editor = {Arias, Joaquín and Batsakis, Sotiris and Faber, Wolfgang and Gupta, Gopal and Pacenza, Francesco and Papadakis, Emmanuel and Robaldo, Livio and Ruckschloss, Kilian and Salazar, Elmer and Saribatur, Zeynep G. and Tachmazidis, Ilias and Weitkamper, Felix and Wyner, Adam}, volume = {3437}, pages = {1--13}, publisher = {CEUR-WS.org}, year = {2023}, url = {https://ceur-ws.org/Vol-3437/paper3PLP.pdf} }

@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} }

*This file was generated by
bibtex2html 1.98.*