2011.bib

@book{RigFabZuf11-BK,
  author = {Fabrizio Riguzzi and Arianna Fabbri and Elena Zuffi},
  title = {Sistemi informativi},
  year = {2011},
  publisher = {Esculapio},
  series = {Collana Progetto Leonardo},
  address = {Bologna, \Italy{} },
  month = {\November{} },
  isbn = {978-88-7488-472-8}
}
@inproceedings{BelLamRigAlb11-URSW11-IW,
  author = {Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi and Simone Albani },
  editor = {Fernando Bobillo and
Rommel Carvalho and
da Costa, Paulo C. G. and
d'Amato, Claudia and
Nicola Fanizzi and
Laskey, Kathryn B. and
Laskey, Kenneth J. and
Thomas Lukasiewicz and
Trevor Martin and
Matthias Nickles and
Michael Pool},
  title = {A Distribution Semantics for Probabilistic Ontologies},
  booktitle = {Proceedings ot the 7th International Workshop on Uncertainty Reasoning for the Semantic Web, Bonn, Germany, 23 October, 2011 },
  year = {2011},
  url = {http://ds.ing.unife.it/~friguzzi/Papers/BelLamRigAlb-URSW11.pdf},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  issn = {1613-0073},
  address = {Aachen, \Germany},
  volume = {778},
  pages = {75-86},
  pdf = {http://ceur-ws.org/Vol-778/paper7.pdf},
  abstract = {We present DISPONTE, a semantics for probabilistic ontologies that is based on the distribution semantics for probabilistic logic programs. In DISPONTE each axiom of a probabilistic ontology is annotated with a probability. The probabilistic theory defines thus a distribution over normal theories (called worlds) obtained by including an axiom in a world with a probability given by the annotation. The probability of a query is  computed from this distribution with marginalization.
We also present the system BUNDLE for reasoning over probabilistic OWL DL ontologies  according to the DISPONTE semantics. BUNDLE is based on Pellet and uses its capability of returning explanations for a query. The explanations are  encoded in a Binary Decision Diagram from which the probability of the query is computed.}
}
@inproceedings{GavRigMil11-AIIA11-IC,
  author = {Marco Gavanelli and Fabrizio Riguzzi  and Michela Milano and Davide Sottara and Alessandro Cangini and Paolo Cagnoli},
  title = {An Application of Fuzzy Logic to Strategic Environmental Assessment},
  booktitle = {Proceedings of the 12th Congress of the Italian Association for Artificial Intelligence, Palermo,  15-17 September 2011 },
  year = {2011},
  editor = {Pirrone, Roberto and Sorbello, Filippo},
  publisher = {Springer},
  address = {Heidelberg, \Germany},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/GavRigMil-AIIA11.pdf},
  url = {http://www.springerlink.com/content/v811466564812126/},
  series = {Lecture Notes in Artificial Intelligence},
  volume = {6934},
  abstract = {Strategic Environmental Assessment (SEA) is used to evaluate the environmental effects of regional plans and programs.
SEA expresses dependencies between  plan activities (infrastructures, plants, resource extractions, buildings, etc.) and environmental pressures, and between these and environmental receptors.
In this paper we employ fuzzy logic and many-valued logics together with numeric transformations for performing SEA. In particular, we discuss four  models that 
capture  alternative interpretations of the dependencies,  combining quantitative and qualitative  information.
We have tested the four models and presented the results to the expert for validation. 
The  interpretability of the results of the models was appreciated by the expert that liked in particular those models returning a possibility distribution in place of a crisp result.
},
  keywords = {Strategic Environmental Assessment, Regional Planning, Fuzzy Logic},
  copyright = {Springer},
  doi = {10.1007/978-3-642-23954-0_30},
  pages = {324-335},
  note = {The original publication is available at \url{http://www.springerlink.com}}
}
@inproceedings{BelRig11-MCP11-IW,
  author = {Elena Bellodi and Fabrizio Riguzzi},
  title = {An {Expectation Maximization} Algorithm for Probabilistic Logic Programs},
  booktitle = {Proceedings of the Workshop on Mining Complex Patterns ({MCP2011}), 17 September 2011},
  address = {Palermo, Italy},
  editor = {Appice, Annalisa and Ceci, Michelangelo and Loglisci, Corrado and Manco, Giuseppe},
  year = {2011},
  month = sep,
  pages = {26-37},
  abstract = {
Recently much work in Machine Learning has concentrated on representation languages able to combine aspects of logic and probability, leading to the birth of a whole field called Statistical Relational Learning.
In this paper we present a technique for parameter learning targeted to a family of formalisms where uncertainty is represented using Logic Programming tools - the so-called Probabilistic Logic Programs such as ICL, PRISM, ProbLog and LPAD.
Since their equivalent Bayesian networks contain hidden variables, an EM algorithm is adopted.
In order to speed the computation, expectations are computed directly on the Binary Decision Diagrams that are built for inference.
The resulting system, called EMBLEM for ``EM over BDDs for probabilistic Logic programs Efficient Mining'', has been applied to a number of datasets and showed good performances both in terms of speed and memory.
},
  url = {http://ds.ing.unife.it/~friguzzi/Papers/BelRig-MCP11.pdf},
  pdf = {http://www.di.uniba.it/~loglisci/MCP2011/proceedings.pdf},
  copyright = {by the authors},
  keywords = { Statistical Relational Learning, Probabilistic Logic Programs, Logic Programs with Annotated Disjunction, Expectation Maximization, Binary Decision Diagrams}
}
@inproceedings{BelRig11-CILC11-NC,
  author = {Elena Bellodi and Fabrizio Riguzzi},
  title = {{EM} over Binary Decision Diagrams for Probabilistic Logic Programs},
  booktitle = {Proceedings of the 26th Italian Conference on Computational Logic ({CILC2011}), Pescara, Italy, 31 August 31-2 September, 2011},
  year = {2011},
  abstract = {
Recently much work in Machine Learning has concentrated on representation languages able to combine aspects of logic and probability, leading to the birth of a whole field called Statistical Relational Learning.
In this paper we present a technique for parameter learning targeted to a family of formalisms where uncertainty is represented using Logic Programming techniques - the so-called Probabilistic Logic Programs such as ICL, PRISM, ProbLog and LPAD.
Since their equivalent Bayesian networks contain hidden variables, an EM algorithm is adopted.
In order to speed the computation, expectations are computed directly on the Binary Decision Diagrams that are built for inference.
The resulting system, called EMBLEM for ``EM over Bdds for probabilistic Logic programs Efficient Mining'', has been applied to a number of datasets and showed good performances both in terms of speed and memory usage.
},
  url = {http://ds.ing.unife.it/~friguzzi/Papers/BelRig-CILC11.pdf},
  copyright = {by the authors},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  issn = {1613-0073},
  address = {Aachen, \Germany},
  volume = {810},
  pdf = {http://ceur-ws.org/Vol-810/paper-l14.pdf},
  pages = {229-243}
}
@inproceedings{Rig11-CILC11-NC,
  author = {Fabrizio Riguzzi},
  title = {{MCINTYRE}: A {Monte Carlo} Algorithm for Probabilistic Logic Programming},
  booktitle = {Proceedings of the 26th Italian Conference on Computational Logic ({CILC2011}),
Pescara, Italy, 31 August-2 September, 2011},
  editor = {Fabio Fioravanti},
  year = {2011},
  abstract = {
Probabilistic Logic Programming is receiving an increasing attention for its ability to model domains with complex and uncertain relations among entities. 
In this paper we concentrate on the problem of approximate inference in probabilistic logic programming languages based on the distribution semantics.
A successful approximate approach is based on Monte Carlo sampling, that consists in verifying the truth of the query in a normal program sampled from the probabilistic program.
The ProbLog system includes such an algorithm and so does the \texttt{cplint} suite.
In this paper we propose an approach for Monte Carlo inference that is based on a program transformation that translates a probabilistic program into a normal program to which the query can be posed. In the transformation, auxiliary atoms are added to the body of rules for performing sampling and checking for the consistency of the sample. The current sample is stored in the internal database of the Yap Prolog engine.
The resulting algorithm, called MCINTYRE for Monte Carlo INference wiTh Yap REcord, is evaluated on various problems: biological networks, artificial datasets and a hidden Markov model.  MCINTYRE is compared with the Monte Carlo algorithms of ProbLog and \texttt{cplint} and with the  exact inference  of the PITA system. The results show  that MCINTYRE is faster than the other Monte Carlo algorithms.
},
  keywords = {Probabilistic Logic Programming,
Monte Carlo Methods,
Logic Programs with Annotated Disjunctions,
ProbLog},
  url = {http://ds.ing.unife.it/~friguzzi/Papers/Rig-CILC11.pdf},
  copyright = {by the author},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  issn = {1613-0073},
  address = {Aachen, \Germany},
  volume = {810},
  pdf = {http://ceur-ws.org/Vol-810/paper-l02.pdf},
  pages = {25--39}
}
@inproceedings{BraRig10-ILP10-IC,
  author = {Stefano Bragaglia and Fabrizio Riguzzi},
  title = {Approximate Inference for
Logic Programs with Annotated Disjunctions},
  booktitle = {Inductive Logic Programming
20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010. Revised Papers },
  volume = {6489},
  pages = {30--37},
  year = {2011},
  address = {Heidelberg, \Germany},
  series = {LNCS},
  editor = {Frasconi, Paolo and Lisi, Francesca},
  publisher = {Springer},
  doi = {10.1007/978-3-642-21295-6_7},
  url = {http://ds.ing.unife.it/~friguzzi/Papers/BraRig-ILP10.pdf},
  pdf = {http://www.springerlink.com/content/2g75wu4571554j0x/},
  note = {The original publication is available at \url{http://www.springerlink.com}},
  copyright = {Springer},
  abstract = {Logic Programs with Annotated Disjunctions (LPADs) are a promising language for Probabilistic Inductive Logic Programming.
In order to develop efficient learning systems for LPADs, it is fundamental to have high\--performing inference algorithms. The existing approaches take too long or fail for large problems. In this paper we adapt to LPAD the approaches for approximate inference that have been developed for ProbLog, namely $k$\--best and Monte Carlo.

$k$\--Best finds a lower bound of the probability of a query by identifying the $k$ most probable explanations while Monte Carlo estimates the probability by smartly sampling the space of programs.
The two techniques have been implemented in the \texttt{cplint} suite and have been tested on real and artificial datasets representing graphs. The results show that both algorithms are able to solve larger problems often in less time than the exact algorithm.},
  keywords = {Probabilistic Inductive Logic Programming, Logic Programs with Annotated Disjunctions, ProbLog},
  scopus = {2s2.079959303593},
  wos = {WOS:000325925600007},
  isbn = {9783642212949}
}
@article{RigSwi11-ICLP11-IJ,
  author = {Fabrizio Riguzzi and Terrance Swift},
  title = {The {PITA} System: Tabling and Answer Subsumption for Reasoning under Uncertainty},
  year = {2011},
  journal = {Theory and Practice of Logic Programming, 27th International
Conference on Logic Programming (ICLP'11) Special Issue, Lexington, Kentucky
6-10 July 2011},
  editor = {John Gallagher and Michael Gelfond},
  volume = {11},
  number = {4--5},
  publisher = {Cambridge University Press},
  copyright = {Cambridge University Press},
  abstract = {Many real world domains require the representation of a measure of
uncertainty.  The most common such representation is probability, and
the combination of probability with logic programs has given rise to
the field of Probabilistic Logic Programming (PLP), leading to
languages such as the Independent Choice Logic, Logic Programs with
Annotated Disjunctions (LPADs), Problog, PRISM and others. These languages
share a similar distribution semantics, and methods have been devised
to translate programs between these languages. 
The complexity of computing the probability of queries to these
general PLP programs is very high due to the need to combine the
probabilities of explanations that may not be exclusive.  As one
alternative, the PRISM system reduces the complexity of query
answering by restricting the form of programs it can evaluate.  As an
entirely different alternative, Possibilistic Logic Programs adopt a
simpler metric of uncertainty than probability.

Each of these approaches -- general PLP, restricted PLP, and
Possibilistic Logic Programming -- can be useful in different domains
depending on the form of uncertainty to be represented, on the form of
programs needed to model problems, and on the scale of the problems to
be solved.  In this paper, we show how the PITA system, which
originally supported the general PLP language of LPADs, can also
efficiently support restricted PLP and Possibilistic Logic Programs.
PITA relies on tabling with answer subsumption and consists of a
transformation along with an API for library functions that interface
with answer subsumption.  We show that, by adapting its transformation
and library functions, PITA can be parameterized to PITA(IND,EXC) 
which supports the restricted PLP of PRISM, including optimizations
that reduce non-discriminating arguments and the computation of
Viterbi paths.  Furthermore, we show PITA to be competitive with PRISM
for complex queries to Hidden Markov Model examples, and sometimes
much faster.
We further show how PITA can be parameterized to PITA(COUNT) which
computes the number of different explanations for a subgoal, and to
PITA(POSS) which scalably implements Possibilistic Logic Programming.
PITA is a supported package in version 3.3 of XSB.
},
  keywords = {Probabilistic Logic Programming, Possibilistic Logic Programming, Tabling, Answer Subsumption, Program Transformation},
  pages = {433--449},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/RigSwi-ICLP11.pdf},
  doi = {10.1017/S147106841100010X},
  http = {http://journals.cambridge.org/repo_A83y6WKy},
  url = {http://arxiv.org/pdf/1107.4747v1},
  arxiv = {1107.4747}
}
@techreport{BelRig11-TR,
  author = {Elena Bellodi and Fabrizio Riguzzi},
  title = { {EM} over Binary Decision Diagrams for Probabilistic Logic Programs},
  year = {2011},
  institution = {Dipartimento di Ingegneria, Universit\`a di Ferrara, Italy},
  number = {CS-2011-01},
  url = {http://ds.ing.unife.it/~friguzzi/Papers/CS-2011-01.pdf}
}
@article{AlbGavLam11-IA-IJ,
  author = {Marco Alberti and Marco Gavanelli and Evelina Lamma and Fabrizio Riguzzi and Sergio Storari},
  title = {Learning specifications of interaction protocols and business processes and proving their properties},
  journal = {Intelligenza artificiale},
  year = 2011,
  volume = 5,
  number = 1,
  pages = {71--75},
  month = feb,
  doi = {10.3233/IA-2011-0006},
  issn = {1724-8035},
  abstract = {In this paper, we overview our recent research
  activity concerning the induction of Logic Programming
  specifications, and the proof of their properties via Abductive
  Logic Programming. Both the inductive and abductive tools here
  briefly described have been applied to respectively learn and verify
  (properties of) interaction protocols in multi-agent systems, Web
  service choreographies, careflows and business processes.},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/AlbGavLam-IA08.pdf}
}

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