@inproceedings{BelRigLam10-KSEM10-IC,
author = {Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma},
title = {Probabilistic Declarative Process Mining},
booktitle = {Proceedings of the 4th International Conference on Knowledge Science, Engineering \& Management ({KSEM 2010}),
Belfast, UK, September 1-3, 2010},
year = {2010},
editor = {Bi, Yaxin and Williams, Mary-Anne},
abstract = {
The management of business processes is receiving much attention, since it can
support signicant eciency improvements in organizations. One of the most
interesting problems is the representation of process models in a language that
allows to perform reasoning on it. Various knowledge-based languages have been
lately developed for such a task and showed to have a high potential due to the
advantages of these languages with respect to traditional graph-based notations.
In this work we present an approach for the automatic discovery of knolwedge-
based process models expressed by means of a probabilistic logic, starting from
a set of process execution traces. The approach first uses the DPML (Declarative
Process Model Learner) algorithm to extract a set of integrity constraints from
a collection of traces. Then, the learned constraints are translated into Markov
Logic formulas and the weights of each formula are tuned using the Alchemy
system. The resulting theory allows to perform probabilistic classication of
traces. We tested the proposed approach on a real database of university
students' careers. The experiments show that the combination of DPML and Alchemy
achieves better results than DPML alone.},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
address = {Heidelberg, \Germany},
volume = {6291},
pages = {292--303},
doi = {10.1007/978-3-642-15280-1_28},
pdf = {http://www.springerlink.com/content/h85k601v74850h5p/},
url = {http://ml.unife.it/wp-content/uploads/Papers/BelRIgLam-KSEM10.pdf},
copyright = {Springer},
note = {The original publication is available at \url{http://www.springerlink.com}}
}
@inproceedings{BelRig12-ILP11-IC,
author = {Elena Bellodi and Fabrizio Riguzzi},
title = {Learning the Structure of Probabilistic Logic Programs},
booktitle = {Inductive Logic Programming
21st International Conference, ILP 2011, London, UK, July 31 - August 3, 2011. Revised Papers },
year = {2012},
editor = {Muggleton, Stephen H. and Tamaddoni-Nezhad, Alireza and Lisi, Francesca A.},
doi = {10.1007/978-3-642-31951-8_10},
series = {LNCS},
volume = {7207},
publisher = {Springer},
address = {Heidelberg, Germany},
pages = {61-75},
note = {The original publication is available at \url{http://www.springerlink.com}},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/BelRig12-ILP11-IC.pdf},
keywords = {Probabilistic Inductive Logic Programming, Logic Programs with Annotated Disjunctions, ProbLog},
copyright = {Springer},
abstract = {There is a growing interest in the field of
Probabilistic Inductive Logic Programming, which uses languages that
integrate logic programming and probability.
Many of these languages are based on the distribution semantics and recently various authors have proposed systems for learning the parameters (PRISM, LeProbLog, LFI-ProbLog
and EMBLEM) or both the structure and the parameters (SEM-CP-logic) of these languages.
EMBLEM for example uses an Expectation Maximization approach in which the expectations are computed on Binary Decision Diagrams.
In this paper we present the algorithm SLIPCASE for ``Structure LearnIng of ProbabilistiC logic progrAmS with Em over bdds''. It performs a beam search in the space of the language of Logic Programs with Annotated Disjunctions (LPAD) using the log likelihood of the data as the guiding heuristics. To estimate the log likelihood of theory refinements it performs a limited number of Expectation Maximization iterations of EMBLEM.
SLIPCASE has been tested on three real world datasets and compared with SEM-CP-logic and Learning using Structural Motifs, an algorithm for Markov Logic Networks. The results show that SLIPCASE achieves higher areas under the precision-recall and ROC curves and is more scalable.
}
}
@inproceedings{RigBelLamZes13-AIIA13-IC,
title = {Computing Instantiated Explanations {in~OWL~DL}},
author = { Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese},
booktitle = {Proceedings of the 13th Conference of the Italian Association for Artificial Intelligence ({AI*IA2013}),
Turin, Italy, 4-6 December 2013},
editor = {Matteo Baldoni and
Cristina Baroglio and Guido Boella},
year = {2013},
pages = {397-408},
volume = {8249},
publisher = {Springer},
copyright = {Springer},
series = {Lecture Notes in Artificial Intelligence},
address = {Heidelberg, Germany},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/RigBelLamZes-AIIA13.pdf},
note = {The original publication is available at
\url{http://link.springer.com}},
doi = {10.1007/978-3-319-03524-6_34}
}
@inproceedings{RigBelLamZese13-RR13b-IC,
title = {{BUNDLE}: A Reasoner for Probabilistic Ontologies},
author = {Fabrizio Riguzzi and Evelina Lamma and Elena Bellodi and Riccardo Zese},
booktitle = {7th International Conference on Web Reasoning and Rule Systems (RR 2013), Mannheim, Germany, July 27-29 2013. Proceedings},
editor = {Faber, Wolfgang and Lembo, Domenico},
year = {2013},
volume = {7994},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
address = {Heidelberg, Germany},
isbn = {978-3-642-39665-6},
copyright = {Springer},
pages = {183-197},
doi = {10.1007/978-3-642-39666-3_14},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/RigBelLam-RR13b.pdf},
abstract = {
Representing uncertain information is very important for modeling real world domains.
Recently, the DISPONTE semantics has been proposed for probabilistic description logics.
In DISPONTE, the axioms of a knowledge base can be annotated with a set of variables and a real number between 0 and 1. This real number represents the probability of each version of the axiom in which the specified variables are instantiated.
In this paper we present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases that follow the $\mathcal{ALC}$ semantics. BUNDLE exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed.
The experiments performed by applying BUNDLE to probabilistic knowledge bases show that it can handle ontologies of realistic size and is competitive with the system PRONTO for the probabilistic description logic P-$\mathcal{SHIQ}$(D).
},
keywords = {Probabilistic Ontologies, Probabilistic Description Logics, OWL, Probabilistic Logic Programming, Distribution Semantics},
note = {The original publication is available at
\url{http://link.springer.com}}
}
@inproceedings{RigBelLamZese13-RR13a-IC,
author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese},
title = {Parameter Learning for Probabilistic Ontologies},
booktitle = {7th International Conference on Web Reasoning and Rule Systems (RR 2013), Mannheim, Germany, July 27-29 2013. Proceedings},
editor = {Faber, Wolfgang and Lembo, Domenico},
year = {2013},
volume = {7994},
isbn = {978-3-642-39665-6},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
address = {Heidelberg, Germany},
copyright = {Springer},
pages = {265-270},
doi = {10.1007/978-3-642-39666-3_26},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/RigBelLam-RR13a.pdf},
note = {The original publication is available at
\url{http://link.springer.com}},
abstract = {Recently, the problem of representing uncertainty in Description Logics (DLs) has received an increasing attention.
In probabilistic DLs, axioms contain numeric parameters that are often difficult to specify or to tune for a human.
In this paper we present an approach for learning and tuning the parameters of
probabilistic ontologies from data. The resulting algorithm,
called EDGE, for Em over bDds for description loGics paramEter learning,
is targeted to DLs following the DISPONTE approach,
that applies the distribution semantics to DLs.},
keywords = {Statistical Relational Learning, Probabilistic Inductive Logic Programming, Probabilistic Logic Programming, Expectation Maximization, Binary Decision Diagrams,
Logic Programs with Annotated Disjunctions}
}
@inproceedings{RigBelLamZes14-ILP13-IC,
author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese},
title = {Learning the Parameters of Probabilistic Description Logics},
booktitle = { Late Breaking papers of the 23rd International Conference on Inductive Logic Programming,
Rio de Janeiro, Brazil, August 28th to 30th, 2013},
editor = {Gerson Zaverucha and Santos Costa, Vitor and Aline Marins Paes},
year = {2014},
volume = {1187},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
address = {Aachen, Germany},
issn = {1613-0073},
url = {http://ceur-ws.org/Vol-1187/paper-08.pdf},
pages = {46-51},
copyright = {by the authors}
}
@inproceedings{GavLamRig15-ICLP-IC,
editor = {De Vos, Marina and
Thomas Eiter and
Yuliya Lierler and
Francesca Toni},
title = {An abductive Framework for {Datalog+-} Ontologies},
author = {Marco Gavanelli and Evelina Lamma and Fabrizio Riguzzi and Elena Bellodi and Riccardo Zese and Giuseppe Cota},
booktitle = {Technical Communications of the 31st Int'l.
Conference on Logic Programming (ICLP 2015)},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
issn = {1613-0073},
address = {Aachen, Germany},
copyright = {by the authors},
year = {2015},
keywords = {Probabilistic Logic Programming, Lifted Inference,
Variable Elimination, Distribution Semantics, ProbLog,
Statistical Relational Artificial Intelligence},
abstract = {
Ontologies are a fundamental component of the Semantic Web since they provide a formal and machine manipulable model of a domain.
Description Logics (DLs) are often the languages of choice for modeling ontologies. Great effort has been spent in identifying decidable or even tractable fragments of DLs. Conversely, for knowledge representation and reasoning, integration with rules and rule-based reasoning is crucial in the so-called Semantic Web stack vision.
Datalog+- is an extension of Datalog which can be used for representing lightweight ontologies, and is able to express
the DL-Lite family of ontology languages, with tractable query answering under certain language restrictions.
In this work, we show that Abductive Logic Programming (ALP) is also a suitable framework for representing Datalog+- ontologies, supporting query answering through an abductive proof procedure, and smoothly achieving the integration of ontologies and rule-based reasoning.
In particular, we consider an Abductive Logic Programming framework named
SCIFF, and derived from the IFF abductive framework, able to deal with existentially (and universally) quantified variables in rule heads, and Constraint Logic Programming constraints.
Forward and backward reasoning is naturally supported in the ALP framework. We show that the SCIFF language smoothly supports the integration of rules, expressed in a Logic Programming language, with Datalog+- ontologies, mapped into SCIFF (forward) integrity constraints.
},
keywords = {Abductive Logic Programming, Datalog+-,
Description Logics,
Semantic Web.},
number = {1433},
url = {http://ceur-ws.org/Vol-1433/tc_89.pdf}
}
@inproceedings{RigBel15-IJCAI-IC,
author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence,
Buenos Aires, Argentina, 25-31 July 2015},
title = {Reasoning with Probabilistic Ontologies},
year = {2015},
editor = {Qiang Yang and Michael Wooldridge},
pages = {4310-4316},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
address = {Palo Alto, California USA},
copyright = {International Joint Conferences on Artificial Intelligence },
isbn = {978-1-57735-738-4},
pdf = {http://ijcai.org/papers15/Papers/IJCAI15-613.pdf},
keywords = {Probabilistic Ontologies, Probabilistic Description Logics, OWL, Probabilistic Logic Programming, Distribution Semantics},
abstract = {Modeling real world domains requires ever more frequently to represent uncertain information.
The DISPONTE semantics for probabilistic description logics allows to annotate axioms of a knowledge base with a value that represents their probability.
In this paper we discuss approaches for performing inference
from probabilistic ontologies following the DISPONTE semantics.
We present the algorithm BUNDLE for computing the probability of queries.
BUNDLE exploits an underlying Description Logic reasoner, such as Pellet, in order to find explanations for a query. These are then encoded in a Binary Decision Diagram that is
used for computing the probability of the query.},
issn = {10450823},
wos = {WOS:000442637804062},
scopus = {2-s2.0-84949759647}
}
@inproceedings{RigBelZes16-ECAI-IC,
year = {2016},
booktitle = {22nd European Conference on Artificial Intelligence {ECAI 2016}},
venue = {The Hague, Netherlands},
eventdate = {August 29-September 2, 2016},
editor = {Maria Fox and Gal Kaminka},
title = {Scaling Structure Learning of Probabilistic Logic Programs by MapReduce},
author = {Fabrizio Riguzzi and Elena Bellodi and Riccardo Zese and Giuseppe Cota and Evelina Lamma},
abstract = {Probabilistic Logic Programming is a promising formalism for dealing with uncertainty. Learning probabilistic logic programs has been receiving an increasing attention in Inductive Logic Programming: for instance,
the system SLIPCOVER learns high quality theories in a variety of domains. However, SLIPCOVER is computationally expensive, with a running time of the order of hours.
In order to apply SLIPCOVER to Big Data, we present SEMPRE, for ``Structure lEarning by MaPREduce", that scales SLIPCOVER by following a MapReduce strategy, directly implemented with the Message Passing Interface. },
keywords = {Probabilistic Logic Programming, Parameter Learning, Structure Learning, MapReduce},
series = {Frontiers in Artificial Intelligence and Applications},
volume = {285},
pages = {1602-1603},
url = {http://ebooks.iospress.nl/volumearticle/44940},
doi = {10.3233/978-1-61499-672-9-1602},
wos = {WOS:000385793700205},
scopus = {2-s2.0-85013029084},
copyright = {CC-BY-NC 4.0},
publisher = {IOS Press}
}
@inproceedings{CotZesBel16-ILP-IC,
booktitle = {Inductive Logic Programming: 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers},
editor = {Katsumi Inoue and Hayato Ohwada and Akihiro Yamamoto},
title = {Distributed Parameter Learning for Probabilistic Ontologies},
author = {Giuseppe Cota and Riccardo Zese and Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/CotZesBel-ILP15.pdf},
year = {2016},
publisher = {Springer International Publishing},
address = {Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = {9575},
copyright = {Springer International Publishing Switzerland},
venue = {Kyoto, Japan},
eventdate = {August 20-22, 2015},
pages = {30--45},
isbn-online = {978-3-319-40566-7},
isbn-print = {978-3-319-40565-0},
issn = {0302-9743},
doi = {10.1007/978-3-319-40566-7_3},
abstract = {Representing uncertainty in Description Logics has recently
received an increasing attention because of its potential to model real
world domains. EDGE for Em over bDds for description loGics param-
Eter learning is an algorithm for learning the parameters of probabilistic
ontologies from data. However, the computational cost of this algorithm
is significant since it may take hours to complete an execution. In this
paper we present EDGEMR, a distributed version of EDGE that exploits
the MapReduce strategy by means of the Message Passing Interface. Ex-
periments on various domains show that EDGEMR signi cantly reduces
EDGE running time.},
keywords = {Probabilistic Description Logics, Parameter Learning, MapReduce,
Message Passing Interface},
note = {The final publication is available at Springer via
\url{http://dx.doi.org/10.1007/978-3-319-40566-7_3}}
}
@inproceedings{GavLamRig17-JURISIN-IC,
author = {Gavanelli, Marco
and Lamma, Evelina
and Riguzzi, Fabrizio
and Bellodi, Elena
and Riccardo, Zese
and Cota, Giuseppe},
editor = {Otake, Mihoko
and Kurahashi, Setsuya
and Ota, Yuiko
and Satoh, Ken
and Bekki, Daisuke},
title = {Abductive Logic Programming for Normative Reasoning and Ontologies},
booktitle = {New Frontiers in Artificial Intelligence: JSAI-isAI 2015 Workshops,
LENLS, JURISIN, AAA, HAT-MASH, TSDAA, ASD-HR, and SKL, Kanagawa, Japan, November 16-18, 2015, Revised Selected Papers},
year = {2017},
publisher = {Springer International Publishing},
copyright = {Springer International Publishing AG},
series = {Lecture Notes in Computer Science},
volume = {10091},
address = {Cham},
pages = {187--203},
isbn-online = {978-3-319-50953-2},
isbn-print = {978-3-319-50952-5},
doi = {10.1007/978-3-319-50953-2_14},
scopus = {2-s2.0-85018397999}
}
@inproceedings{CotRigZes18-SUM-IC,
title = {A Modular Inference System for Probabilistic Description Logics},
author = {Giuseppe Cota and Fabrizio Riguzzi and Riccardo Zese and Elena Bellodi and Evelina Lamma },
booktitle = {Scalable Uncertainty Management 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings},
year = 2018,
editor = {Davide Ciucci and Gabriella Pasi and Barbara Vantaggi},
volume = {11142},
publisher = {Springer},
address = {Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
issn = {1613-0073},
doi = {10.1007/978-3-030-00461-3_6},
venue = {Milano, Italy},
eventdate = {October 3-5, 2018},
copyright = {Springer},
pages = {78-92},
isbn-print = {978-3-030-00460-6},
isbn-online = {978-3-030-00461-3},
note = {The final publication is available at Springer via \url{http://dx.doi.org/10.1007/978-3-030-00461-3_6}},
url = {http://ml.unife.it/wp-content/uploads/Papers/CotRigZes-SUM18.pdf},
scopus = {2-s2.0-85054858704}
}
@inproceedings{BelBerGavZes20-AIXIA-IC,
author = {Elena Bellodi and
Alessandro Bertagnon and
Marco Gavanelli and
Riccardo Zese},
editor = {Matteo Baldoni and
Stefania Bandini},
title = {Improving the Efficiency of Euclidean {TSP} Solving in Constraint
Programming by Predicting Effective Nocrossing Constraints},
booktitle = {AIxIA 2020 - Advances in Artificial Intelligence - XIXth International
Conference of the Italian Association for Artificial Intelligence,
Virtual Event, November 25-27, 2020, Revised Selected Papers},
series = {Lecture Notes in Computer Science},
volume = {12414},
pages = {318--334},
publisher = {Springer},
year = {2020},
url = {https://doi.org/10.1007/978-3-030-77091-4\_20},
doi = {10.1007/978-3-030-77091-4\_20},
timestamp = {Tue, 15 Jun 2021 01:00:00 +0200},
biburl = {https://dblp.org/rec/conf/aiia/BellodiBGZ20a.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{AzzBelBraRigLam20-ICLP-IC,
title = {Modeling Bitcoin Lightning Network by Logic Programming},
booktitle = {Proceedings of the 36th International Conference on Logic Programming (Technical Communications)},
year = 2020,
author = {Damiano Azzolini and Elena Bellodi and Alessandro Brancaleoni and Fabrizio Riguzzi and Evelina Lamma},
editor = {Francesco Ricca and Alessandra Russo and Sergio Greco and Nicola Leone and Alexander Artikis and Gerhard Friedrich and Paul Fodor and Angelika Kimmig and Francesca Lisi and Marco Maratea and Alessandra Mileo and Fabrizio Riguzzi},
publisher = {Open Publishing Association},
address = {Waterloo, Australia},
issn = {2075-2180},
venue = {Rende, Italy},
eventdate = {September 18-25, 2020},
copyright = {by the authors},
url = {https://cgi.cse.unsw.edu.au/~eptcs/content.cgi?ICLP2020#EPTCS325.30},
http = {https://arxiv.org/html/2009.09158v1/#EPTCS325.30},
doi = {10.4204/EPTCS.325},
scopus = {2-s2.0-85092656765},
pages = {258--260}
}
@inproceedings{BelZesBer21-LOD-IC,
title = {Machine Learning in a Policy Support System for Smart Tourism Management},
author = {Elena Bellodi and Riccardo Zese and Francesco Bertasi},
booktitle = {Proceedings of the 7th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science - LOD, October 4 – 8, 2021 – Grasmere, Lake District, England – UK},
year = 2021,
publisher = {Springer Nature},
address = {Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
venue = {Online and Grasmere, Lake District, UK},
eventdate = {October 4 – 8, 2021},
copyright = {Springer},
volume = {In press}
}
@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}
}
@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}
}
@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{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{AzzBelRig24-ILP-IC,
author = {Azzolini, Damiano and Bellodi, Elena and Riguzzi, Fabrizio},
editor = {Muggleton, Stephen H. and Tamaddoni-Nezhad, Alireza},
title = {Learning the Parameters of Probabilistic Answer Set Programs},
booktitle = {Inductive Logic Programming - ILP 2022},
year = {2024},
publisher = {Springer Nature Switzerland},
address = {Cham},
pages = {1--14},
isbn = {978-3-031-55630-2},
series = {Lecture Notes in Computer Science},
volume = {14363},
doi = {10.1007/978-3-031-55630-2_1},
url = {https://link.springer.com/chapter/10.1007/978-3-031-55630-2_1}
}
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