@inproceedings{AlbBelCot16-PLP-IW, title = {Probabilistic Constraint Logic Theories}, author = {Marco Alberti and Elena Bellodi and Giuseppe Cota and Evelina Lamma and Fabrizio Riguzzi and Riccardo Zese}, pages = {15--28}, url = {http://ceur-ws.org/Vol-1661/#paper-02}, pdf = {http://ceur-ws.org/Vol-1661/paper-02.pdf}, booktitle = {Proceedings of the 3nd International Workshop on Probabilistic Logic Programming ({PLP})}, year = 2016, editor = {Arjen Hommersom and Samer Abdallah}, volume = 1661, series = {CEUR Workshop Proceedings}, address = {Aachen, Germany}, issn = {1613-0073}, venue = {London, UK}, eventdate = {2016-09-03}, publisher = {Sun {SITE} Central Europe}, copyright = {by the authors}, abstract = {Probabilistic logic models are used ever more often to deal with the uncertain relations typical of the real world. However, these models usually require expensive inference procedures. Very recently the problem of identifying tractable languages has come to the fore. In this paper we consider the models used by the learning from interpretations ILP setting, namely sets of integrity constraints, and propose a probabilistic version of them. A semantics in the style of the distribution semantics is adopted, where each integrity constraint is annotated with a probability. These probabilistic constraint logic models assign a probability of being positive to interpretations. This probability can be computed in a time that is logarithmic in the number of ground instantiations of violated constraints. This formalism can be used as the target language in learning systems and for declaratively specifying the behavior of a system. In the latter case, inference corresponds to computing the probability of compliance of a system's behavior to the model. }, keywords = { Probabilistic Logic Programming, Distribution Semantics, Constraint Logic Theories}, scopus = {2-s2.0-84987763948} }

@inproceedings{AlbCotRigZes16-AIIA-IC, booktitle = {Proceedings of the 15th Conference of the Italian Association for Artificial Intelligence ({AI*IA2016}), Genova, Italy, 28 November - 1 December 2016}, editor = {Giovanni Adorni and Stefano Cagnoni and Marco Gori and Marco Maratea}, year = {2016}, title = {Probabilistic Logical Inference On the Web}, author = {Marco Alberti and Giuseppe Cota and Fabrizio Riguzzi and Riccardo Zese}, abstract = {cplint on SWISH is a web application for probabilistic logic programming. It allows users to perform inference and learning using just a web browser, with the computation performed on the server. In this paper we report on recent advances in the system, namely the inclusion of algorithms for computing conditional probabilities with exact, rejection sampling and Metropolis-Hasting methods. Moreover, the system now allows hybrid programs, i.e., programs where some of the random variables are continuous. To perform inference on such programs likelihood weighting is used that makes it possible to also have evidence on continuous variables. cplint on SWISH offers also the possibility of sampling arguments of goals, a kind of inference rarely considered but useful especially when the arguments are continuous variables. Finally, cplint on SWISH offers the possibility of graphing the results, for example by drawing the distribution of the sampled continuous arguments of goals.}, publisher = {Springer International Publishing}, address = {Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, volume = {10037}, copyright = {Springer International Publishing AG}, keywords = {Probabilistic Logic Programming, Probabilistic Logical Inference, Hybrid program}, pdf = {http://ml.unife.it/wp-content/uploads/Papers/AlbCotRig-AIXIA16.pdf}, doi = {10.1007/978-3-319-49130-1_26}, pages = {351-363}, venue = {Genova, Italy}, eventdate = {November 28-December 1, 2016}, isbn-online = {978-3-319-49129-5}, isbn-print = {978-3-319-49130-1}, issn = {0302-9743}, scopus = {2-s2.0-85006074125}, wos = {WOS:000389797400026}, note = {The final publication is available at Springer via \url{http://dx.doi.org/10.1007/978-3-319-49130-1_26}} }

@inproceedings{AlbLamRigZes16-AIIA-IC, booktitle = {Proceedings of the 15th Conference of the Italian Association for Artificial Intelligence ({AI*IA2016}), Genova, Italy, 28 November - 1 December 2016}, editor = {Giovanni Adorni and Stefano Cagnoni and Marco Gori and Marco Maratea}, year = {2016}, title = {Probabilistic Hybrid Knowledge Bases under the Distribution Semantics}, author = {Marco Alberti and Evelina Lamma and Fabrizio Riguzzi and Riccardo Zese}, abstract = {Since Logic Programming (LP) and Description Logics (DLs) are based on different assumptions (the closed and the open world assumption, respectively), combining them provides higher expressiveness in applications that require both assumptions. Several proposals have been made to combine LP and DLs. An especially successful line of research is the one based on the Lifschitz's logic of Minimal Knowledge with Negation as Failure (MKNF). Motik and Rosati introduced Hybrid knowledge bases (KBs), composed of LP rules and DL axioms, gave them an MKNF semantics and studied their complexity. Knorr et al. proposed a well-founded semantics for Hybrid KBs where the LP clause heads are non-disjunctive, which keeps querying polynomial (provided the underlying DL is polynomial) even when the LP portion is non-stratified. In this paper, we propose Probabilistic Hybrid Knowledge Bases (PHKBs), where the atom in the head of LP clauses and each DL axiom is annotated with a probability value. PHKBs are given a distribution semantics by defining a probability distribution over deterministic Hybrid KBs. The probability of a query being true is the sum of the probabilities of the deterministic KBs that entail the query. Both epistemic and statistical probability can be addressed, thanks to the integration of probabilistic LP and DLs.}, publisher = {Springer International Publishing}, address = {Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, volume = {10037}, copyright = {Springer International Publishing AG}, issn = {0302-9743}, keywords = {Probabilistic Logic Programming, Probabilistic Description Logics, Hybrid Knowledge Bases}, pdf = {http://ml.unife.it/wp-content/uploads/Papers/AlbLamRig-AIXIA16.pdf}, doi = {10.1007/978-3-319-49130-1_27}, pages = {364-376}, venue = {Genova, Italy}, scopus = {2-s2.0-85005950065}, wos = {WOS:000389797400027}, eventdate = {November 28-December 1, 2016}, isbn-online = {978-3-319-49129-5}, isbn-print = {978-3-319-49130-1}, note = {The final publication is available at Springer via \url{http://dx.doi.org/10.1007/978-3-319-49130-1_27}} }

*This file was generated by
bibtex2html 1.98.*