@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.