1579 lines
54 KiB
BibTeX
1579 lines
54 KiB
BibTeX
@article{DBLP:journals/ai/Cohen95,
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||
author = {William W. Cohen},
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||
title = {Pac-Learning Non-Recursive Prolog Clauses},
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journal = {Artif. Intell.},
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volume = {79},
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||
number = {1},
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||
year = {1995},
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||
pages = {1-38},
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||
ee = {http://dx.doi.org/10.1016/0004-3702(94)00034-4},
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||
bibsource = {DBLP, http://dblp.uni-trier.de}
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||
}
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||
@article{BelRig13-TPLP-IJ,
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||
author = {Elena Bellodi and Fabrizio Riguzzi},
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title = {Structure Learning of Probabilistic Logic Programs by Searching the Clause Space},
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||
journal = {Theory and Practice of Logic Programming},
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||
publisher = {Cambridge University Press},
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||
copyright = {Cambridge University Press},
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year = {2013}
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||
}
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||
@article{BelRig11-IDA,
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author = {Elena Bellodi and Fabrizio Riguzzi},
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||
title = { Expectation {Maximization} over Binary Decision Diagrams for Probabilistic Logic Programs},
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||
year = {2012},
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||
volume={16},
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||
number={6},
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||
journal={Intel. Data Anal.},
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||
}
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||
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||
@article{DBLP:journals/jmlr/ElidanF05,
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||
author = {G. Elidan and
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||
N. Friedman},
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||
title = {Learning Hidden Variable Networks: The Information Bottleneck
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||
Approach},
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||
journal = {Journal of Machine Learning Research},
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||
volume = {6},
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||
year = {2005},
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||
pages = {81-127},
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||
ee = {http://www.jmlr.org/papers/v6/elidan05a.html},
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||
bibsource = {DBLP, http://dblp.uni-trier.de}
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||
}
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||
@inproceedings{BraRig10-ILP10-IC,
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||
author = {Stefano Bragaglia and Fabrizio Riguzzi},
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||
title = {Approximate Inference for
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||
Logic Programs with Annotated Disjunctions},
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||
booktitle = {Inductive Logic Programming
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20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010. Revised Papers },
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||
volume={6489},
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||
pages={30--37},
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||
year = {2011},
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||
|
||
series = {LNCS},
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||
editor = {Frasconi, Paolo and Lisi, Francesca},
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||
publisher = {Springer},
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||
doi = {10.1007/978-3-642-21295-6_7},
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||
url={http://www.ing.unife.it/docenti/FabrizioRiguzzi/Papers/BraRig-ILP10.pdf},
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||
copyright={Springer},
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||
}
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||
@inproceedings{Rig11-CILC11-NC,
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author = {Fabrizio Riguzzi},
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title = {{MCINTYRE}: A {Monte Carlo} Algorithm for Probabilistic Logic Programming},
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booktitle = {Proceedings of the 26th Italian Conference on Computational Logic ({CILC2011}),
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||
Pescara, Italy, 31 August-2 September, 2011},
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||
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year = {2011},
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abstract={
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Probabilistic Logic Programming is receiving an increasing attention for its ability to model domains with complex and uncertain relations among entities.
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In this paper we concentrate on the problem of approximate inference in probabilistic logic programming languages based on the distribution semantics.
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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.
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The ProbLog system includes such an algorithm and so does the \texttt{cplint} suite.
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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.
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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.
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},
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url={http://www.ing.unife.it/docenti/FabrizioRiguzzi/Papers/Rig-CILC11.pdf},
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copyright={by the author},
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}
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@inproceedings{BelRig11-CILC11-NC,
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author = {Elena Bellodi and Fabrizio Riguzzi},
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title = {{EM} over Binary Decision Diagrams for Probabilistic Logic Programs},
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booktitle = {Proceedings of the 26th Italian Conference on Computational Logic ({CILC2011}),
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Pescara, Italy, 31 August 31-2 September, 2011},
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||
|
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year = {2011},
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abstract={
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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.
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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.
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Since their equivalent Bayesian networks contain hidden variables, an EM algorithm is adopted.
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In order to speed the computation, expectations are computed directly on the Binary Decision Diagrams that are built for inference.
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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.
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},
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url={http://www.ing.unife.it/docenti/FabrizioRiguzzi/Papers/BelRig-CILC11.pdf},
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||
copyright={by the authors},
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||
}
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||
|
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@inproceedings{BelRig11-ILP11-IC,
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author = {Elena Bellodi and Fabrizio Riguzzi},
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title = {Learning the Structure of Probabilistic Logic Programs},
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||
booktitle = {Inductive Logic Programming,
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||
21th International Conference, ILP 2011, London, UK, 31 July-3 August, 2011 },
|
||
year = {2011},
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||
url={http://ilp11.doc.ic.ac.uk/short_papers/ilp2011_submission_52.pdf},
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||
}
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||
|
||
@article{RigDiM11-ML-IJ,
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||
author = {Fabrizio Riguzzi and Nicola Di Mauro},
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||
title = {Applying the Information Bottleneck to Statistical Relational Learning},
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year = {2011},
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journal={Machine Learning},
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pdf={http://www.ing.unife.it/docenti/FabrizioRiguzzi/Papers/RigDiM11-ML-IJ.pdf},
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note={To appear},
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doi = {10.1007/s10994-011-5247-6},
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publisher={Springer},
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copyright={Springer},
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abstract={In this paper we propose to apply the Information Bottleneck (IB) approach to the sub-class of
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Statistical Relational Learning (SRL) languages that are reducible to Bayesian networks. When the
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resulting networks involve hidden variables, learning these languages requires the use of techniques
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for learning from incomplete data such as the Expectation Maximization (EM) algorithm.
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Recently, the IB approach was shown to be able to avoid some of the local maxima in which EM can get
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trapped when learning with hidden variables. Here we present the algorithm Relational Information Bottleneck
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(RIB)
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that learns the parameters of SRL languages reducible
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to Bayesian Networks.
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In particular, we present the specialization of RIB to a language belonging to the family of languages based on the distribution semantics, Logic Programs with Annotated Disjunction (LPADs). This language is prototypical for such a family and its equivalent Bayesian networks contain hidden variables. RIB is evaluated on the IMDB, Cora and artificial datasets and compared with LeProbLog, EM, Alchemy and PRISM.
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The experimental results show that RIB has good performances especially when some logical atoms are unobserved.
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Moreover, it is particularly suitable when learning from interpretations that share the same Herbrand base.},
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||
}
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||
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@techreport{BelRig11-TR,
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author = {Elena Bellodi and Fabrizio Riguzzi},
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title = { {EM} over Binary Decision Diagrams for Probabilistic Logic Programs},
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year = {2011},
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||
institution = {Dipartimento di Ingegneria, Universit\`a di Ferrara, Italy},
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number={CS-2011-01},
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||
url={http://www.unife.it/dipartimento/ingegneria/informazione/informatica/rapporti-tecnici-1/CS-2011-01.pdf/view}
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||
}
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||
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@inproceedings{Rig-RCRA07-IC,
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author={
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Fabrizio Riguzzi },
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||
title={A Top Down Interpreter for {LPAD} and {CP}\--logic},
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booktitle={Proceedings of the 14th RCRA workshop
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Experimental Evaluation of Algorithms for
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Solving Problems with Combinatorial Explosion},
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||
year={2007},
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||
pdf={http://pst.istc.cnr.it/RCRA07/articoli/P19-riguzzi-RCRA07.pdf},
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||
abstract={Logic Programs with Annotated Disjunctions and CP-logic are two different but related languages for expressing probabilistic information in logic programming.
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||
The paper presents a top down interpreter for computing the probability of a query from a program in one of these two languages when the program is acyclic.
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||
The algorithm is based on the one available for ProbLog.
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The performances of the algorithm are compared with those of a Bayesian reasoner and with those of the ProbLog interpreter. On programs that have a small grounding, the Bayesian reasoner is more scalable, but programs with a large grounding require the top down interpreter. The comparison with ProbLog shows that, even if the added expressiveness effectively requires more computation resources, the top down interpreter can still solve
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problem of significant size.
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||
},
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keywords={Probabilistic Logic Programming, Logic Programs with Annotated Disjunction, Probabilistic Reasoning},
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||
}
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||
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@techreport{VenVer03-TR,
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||
author = {J. Vennekens and S. Verbaeten},
|
||
title = {Logic Programs With Annotated Disjunctions},
|
||
year = {2003},
|
||
institution = {K. U. Leuven},
|
||
number = {CW386},
|
||
url = {http://www.cs.kuleuven.ac.be/\string~joost/techrep.ps},
|
||
}
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||
|
||
@inProceedings{VenVer04-ICLP04-IC,
|
||
author = {J. Vennekens and S. Verbaeten and M. Bruynooghe},
|
||
title = {Logic Programs With Annotated Disjunctions},
|
||
booktitle = {International Conference on Logic Programming},
|
||
year = {2004},
|
||
series={LNCS},
|
||
volume={3131},
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||
publisher={Springer},
|
||
pages={195-209}
|
||
|
||
|
||
}
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||
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||
@inproceedings{RigSwi10-ICLP10-IC,
|
||
author = {Fabrizio Riguzzi and Terrance Swift},
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||
title = {{T}abling and {A}nswer {S}ubsumption for {R}easoning on {L}ogic {P}rograms with {A}nnotated {D}isjunctions},
|
||
booktitle = {Technical Communications of the International
|
||
Conference on Logic Programming},
|
||
volume = {7},
|
||
year = {2010},
|
||
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
|
||
series = {Leibniz International Proceedings in
|
||
Informatics (LIPIcs)},
|
||
ISBN = {978-3-939897-17-0},
|
||
ISSN = {1868-8969},
|
||
pages = {162--171},
|
||
doi = {10.4230/LIPIcs.ICLP.2010.162}
|
||
}
|
||
|
||
@inproceedings{DBLP:conf/iclp/MantadelisJ10,
|
||
author = {Theofrastos Mantadelis and
|
||
Gerda Janssens},
|
||
title = {Dedicated Tabling for a Probabilistic Setting},
|
||
booktitle = {International Conference
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||
on Logic Programming},
|
||
year = {2010},
|
||
pages = {124-133},
|
||
series = {LIPIcs},
|
||
volume = {7},
|
||
ee = {http://dx.doi.org/10.4230/LIPIcs.ICLP.2010.124},
|
||
publisher = {Schloss Dagstuhl - LZI},
|
||
|
||
}
|
||
booktitle = {ICLP (Technical Communications)},
|
||
editor = {Manuel V. Hermenegildo and
|
||
Torsten Schaub},
|
||
|
||
|
||
@inproceedings{CCIL08,
|
||
author = "F. Calimeri and S. Cozza and G. Ianni and N. Leone",
|
||
title = "Computable Functions in {ASP}: Theory and Implementation",
|
||
booktitle = ICLP,
|
||
publisher = {Springer},
|
||
series = {LNCS},
|
||
volume = {5366},
|
||
pages = "407-424",
|
||
year = 2008}
|
||
|
||
|
||
@article{BaBC09,
|
||
author = "S. Baselice and P. Bonatti and G. Criscuolo",
|
||
title = "On finitely recursive programs",
|
||
journal = TPLP,
|
||
Volume = 9,
|
||
Number = 2,
|
||
pages = "213-238",
|
||
year = 2009}
|
||
|
||
@article{Swif99a,
|
||
AUTHOR = "T. Swift",
|
||
TITLE = "Tabling for Non-Monotonic Programming",
|
||
Journal = {Annals of Mathematics and Artifial Intelligence},
|
||
publisher = {Baltzer Science Publishers},
|
||
volume = {25},
|
||
number = {3-4},
|
||
pages = "201-240",
|
||
year = {1999}
|
||
}
|
||
|
||
@article{ SaSW99,
|
||
AUTHOR = "K. Sagonas and T. Swift and D. S. Warren",
|
||
TITLE = "The Limits of Fixed-Order Computation",
|
||
Journal = "Theoretical Computer Science",
|
||
Volume = 254,
|
||
Number = "1-2",
|
||
Pages = "465-499",
|
||
Year = 2000 }
|
||
|
||
% only used in appendix.
|
||
@inproceedings{Swif99b,
|
||
AUTHOR = "T. Swift",
|
||
TITLE = "A New Formulation of Tabled Resolution with Delay",
|
||
Booktitle = "Portuguese Conference
|
||
on Artificial Intelligence",
|
||
Pages = "163-177",
|
||
Year = 1999,
|
||
Series = "LNAI",
|
||
volume = 1695,
|
||
Publisher = "Springer",
|
||
}
|
||
|
||
@inproceedings{Przy89d,
|
||
AUTHOR = "T. Przymusinski",
|
||
TITLE = "Every Logic Program has a Natural Stratification and an Iterated Least Fixed Point Model",
|
||
BOOKTITLE = "Symposium on Principles of Database Systems",
|
||
PAGES = "11-21",
|
||
YEAR = "1989",
|
||
publisher = {ACM Press},
|
||
}
|
||
|
||
@inproceedings{DBLP:conf/cl/KameyaS00,
|
||
author = {Yoshitaka Kameya and
|
||
Taisuke Sato},
|
||
title = "Efficient {EM} Learning with Tabulation for Parameterized
|
||
Logic Programs",
|
||
booktitle = {First International Conference on Computational Logic},
|
||
year = {2000},
|
||
pages = {269-284},
|
||
ee = {http://link.springer.de/link/service/series/0558/bibs/1861/18610269.htm},
|
||
publisher = {Springer},
|
||
series = {LNCS},
|
||
volume = {1861},
|
||
bibsource = {DBLP, http://dblp.uni-trier.de}
|
||
}
|
||
|
||
|
||
|
||
@inproceedings{DBLP:conf/iclp/KimmigCRDR08,
|
||
author = "Angelika Kimmig and
|
||
V\'{\i}tor {Santos Costa} and
|
||
Ricardo Rocha and
|
||
Bart Demoen and
|
||
Luc {De Raedt}",
|
||
title = "On the Efficient Execution of {ProbLog} Programs",
|
||
booktitle = {International Conference on Logic Programming},
|
||
year = {2008},
|
||
pages = {175-189},
|
||
ee = {http://dx.doi.org/10.1007/978-3-540-89982-2_22},
|
||
bibsource = {DBLP, http://dblp.uni-trier.de},
|
||
publisher = {Springer},
|
||
series = {LNCS},
|
||
volume = {5366}
|
||
}
|
||
|
||
|
||
@inproceedings{DeR-NIPS08,
|
||
author={De Raedt, L. and Demoen, B. and
|
||
Fierens, D. and
|
||
Gutmann, B. and Janssens, G. and
|
||
Kimmig, A. and Landwehr, N. and
|
||
Mantadelis, T. and
|
||
Meert, W. and
|
||
Rocha, R. and
|
||
Santos Costa, V. and
|
||
Thon, I. and
|
||
Vennekens, J.},
|
||
title={Towards digesting the alphabet-soup of statistical relational learning},
|
||
booktitle={{NIPS*2008} Workshop on Probabilistic Programming},
|
||
year={2008}
|
||
}
|
||
|
||
% TLS: took out address for space.
|
||
@inproceedings{KimGutSan-ILP09-IC,
|
||
author = "A. Kimmig and B. Gutmann and V. {Santos Costa}",
|
||
title= "Trading Memory for Answers: Towards Tabling {ProbLog}",
|
||
booktitle = {International Workshop on Statistical Relational Learning},
|
||
publisher = {KU Leuven},
|
||
year = {2009},
|
||
}
|
||
% address={Leuven, Belgium}
|
||
|
||
@book{pearl88,
|
||
title = {Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference},
|
||
author = {Judea Pearl},
|
||
publisher = {Morgan Kaufmann},
|
||
year = {1988},
|
||
isbn = {1558604790},
|
||
keywords = {imported intelligend probabilistic reasoning systems }
|
||
}
|
||
|
||
@article{DBLP:journals/tplp/VennekensDB09,
|
||
author = {J. Vennekens and
|
||
Marc Denecker and
|
||
Maurice Bruynooghe},
|
||
title = {{CP}-logic: A language of causal probabilistic events and
|
||
its relation to logic programming},
|
||
journal = {Theory Pract. Log. Program.},
|
||
volume = {9},
|
||
number = {3},
|
||
year = {2009},
|
||
pages = {245-308},
|
||
ee = {http://dx.doi.org/10.1017/S1471068409003767},
|
||
bibsource = {DBLP, http://dblp.uni-trier.de}
|
||
}
|
||
|
||
@article{NgSub-InfComp91,
|
||
author = {Ng, Raymond and Subrahmanian, V. S.},
|
||
title = {Probabilistic logic programming},
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||
journal = {Inf. Comput.},
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||
volume = {101},
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number = {2},
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year = {1992},
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issn = {0890-5401},
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pages = {150--201},
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doi = {http://dx.doi.org/10.1016/0890-5401(92)90061-J},
|
||
publisher = {Academic Press, Inc.},
|
||
address = {Duluth, MN, USA},
|
||
}
|
||
|
||
@article{Emd-JLP86,
|
||
author = "{van Emden}, M H",
|
||
title = {Quantitative deduction and its fixpoint theory},
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||
journal = {J. Log. Program.},
|
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volume = {30},
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number = {1},
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year = {1986},
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issn = {0743-1066},
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||
pages = {37--53},
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||
publisher = {Elsevier Science Inc.},
|
||
address = {New York, NY, USA},
|
||
}
|
||
|
||
@inproceedings{Sha-IJCAI83,
|
||
author = {Shapiro, Ehud Y.},
|
||
title = {Logic programs with uncertainties: a tool for implementing rule-based systems},
|
||
booktitle = IJCAI,
|
||
year = {1983},
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||
pages = {529--532},
|
||
publisher = {Morgan Kaufmann Publishers Inc.},
|
||
}
|
||
location = {Karlsruhe, West Germany},
|
||
address = {San Francisco, CA, USA},
|
||
% booktitle = {International Joint conference on Artificial intelligence},
|
||
|
||
@inproceedings{Rig09-RCRA-IW,
|
||
author={F. Riguzzi},
|
||
title={The {SLGAD} Procedure for Inference on {Logic Programs with Annotated
|
||
Disjunctions}},
|
||
booktitle={Proceedings of the 15th {RCRA} workshop on Experimental Evaluation
|
||
of Algorithms for Solving Problems with Combinatorial Explosion
|
||
Udine, Italy, December 12-13, 2008},
|
||
editor={M. Gavanelli and T. Mancini},
|
||
url={http://ceur-ws.org/Vol-451/paper15riguzzi.pdf},
|
||
series={CEUR Workshop Proceedings},
|
||
publisher={Sun {SITE} Central Europe},
|
||
issn={1613-0073},
|
||
number={451},
|
||
year={2009},
|
||
address={Aachen, Germany},
|
||
}
|
||
|
||
|
||
|
||
|
||
@ARTICLE{DBLP:journals/jlp/ChenSW95,
|
||
author = {Weidong Chen and Terrance Swift and David Scott Warren},
|
||
title = {Efficient Top-Down Computation of Queries under the Well-Founded
|
||
Semantics},
|
||
journal = {J. Log. Program.},
|
||
year = {1995},
|
||
volume = {24},
|
||
pages = {161-199},
|
||
number = {3}
|
||
}
|
||
|
||
@inproceedings{MeeStrBlo08-ILP09-IC,
|
||
author = {W. Meert and J. Struyf and H. Blockeel},
|
||
title = {{CP-Logic} Theory Inference with Contextual Variable Elimination and Comparison to {BDD} Based Inference Methods},
|
||
booktitle = {International Conference on Inductive Logic Programming},
|
||
year = {2009},
|
||
publisher = {KU LEuven},
|
||
}
|
||
address={Leuven, Belgium},
|
||
|
||
@article{DBLP:journals/jacm/ChenW96,
|
||
author = {Weidong Chen and
|
||
David Scott Warren},
|
||
title = {Tabled Evaluation With Delaying for General Logic Programs},
|
||
journal = JACM,
|
||
volume = {43},
|
||
number = {1},
|
||
year = {1996},
|
||
pages = {20-74},
|
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ee = {db/journals/jacm/ChenW96.html, http://doi.acm.org/10.1145/227595.227597},
|
||
bibsource = {DBLP, http://dblp.uni-trier.de}
|
||
}
|
||
@article{Rig09-LJIGPL-IJ,
|
||
author = {Fabrizio Riguzzi},
|
||
title = {Extended Semantics and Inference for the {Independent Choice Logic}},
|
||
journal = {Logic Journal of the IGPL},
|
||
publisher = {Oxford University Press},
|
||
volume = {17},
|
||
number = {6},
|
||
pages = {589--629},
|
||
address = {Oxford, \UK},
|
||
year = {2009},
|
||
abstract = {The Independent Choice Logic (ICL) is a language for expressing
|
||
probabilistic information in logic programming that adopts a distribution
|
||
semantics: an ICL theory defines a distribution over a set of possible worlds
|
||
that are normal logic programs. The probability of a query is then given by the
|
||
sum of the probabilities of worlds where the query is true.
|
||
|
||
The ICL semantics requires the theories to be acyclic. This is a strong
|
||
limitation that rules out many interesting programs.
|
||
In this paper we present an extension of the ICL semantics that allows theories
|
||
to be modularly acyclic.
|
||
|
||
Inference with ICL can be performed with the Cilog2 system that computes
|
||
explanations to queries and then makes them mutually incompatible by means of
|
||
an iterative algorithm.
|
||
|
||
We propose the system PICL (for Probabilistic inference with ICL) that computes
|
||
the explanations to queries by means of a modification of SLDNF\--resolution
|
||
and then makes them mutually incompatible by means of Binary Decision Diagrams.
|
||
|
||
PICL and Cilog2 are compared on problems that involve computing the probability
|
||
of a connection between two nodes in biological graphs and social networks.
|
||
PICL turned to be more efficient, handling larger networks/more complex queries
|
||
in a shorter time than Cilog2. This is true both for marginal and for
|
||
conditional queries.
|
||
},
|
||
doi = {10.1093/jigpal/jzp025},
|
||
url = {http://jigpal.oxfordjournals.org/cgi/reprint/jzp025?ijkey=picqzY6rpyU6emf&keytype=ref },
|
||
http = {http://jigpal.oxfordjournals.org/cgi/content/abstract/jzp025?ijkey=picqzY6rpyU6emf&keytype=ref },
|
||
keywords = {Probabilistic Logic Programming, Independent Choice Logic, Modularly acyclic programs, SLDNF-Resolution},
|
||
copyright = {Fabrizio Riguzzi, exclusively licensed to Oxford University Press}
|
||
}
|
||
|
||
|
||
|
||
@inproceedings{Rig08-ICLP08-IC,
|
||
author = {F. Riguzzi},
|
||
title = {Inference with Logic Programs with Annotated Disjunctions under the Well Founded Semantics},
|
||
booktitle = ICLP,
|
||
publisher = {Springer},
|
||
series = {LNCS},
|
||
year = {2008},
|
||
volume={5366},
|
||
pages={667-771},
|
||
pdf={http://www.ing.unife.it/docenti/FabrizioRiguzzi/Papers/Rig-ICLP08.pdf},
|
||
doi={10.1007/978-3-540-89982-2\string_54},
|
||
}
|
||
url={http://www.springerlink.com/content/247533616617llm8/}
|
||
|
||
@article{Rig09-JACIL-IJ,
|
||
author={F. Riguzzi},
|
||
title={{SLGAD} Resolution for Inference on {Logic Programs with Annotated
|
||
Disjunctions}},
|
||
journal={Journal of Algorithms in Logic, Informatics and Cognition },
|
||
publisher={Elsevier},
|
||
note={,\ in press},
|
||
year=2009,
|
||
abstract={Logic Programs with Annotated Disjunctions (LPADs) allow to express
|
||
probabilistic information in logic programming. The semantics of an LPAD is
|
||
given in terms of well\--founded models of the normal logic programs obtained
|
||
by selecting one disjunct from each ground LPAD clause.
|
||
|
||
Inference on LPADs can be performed using either the system Ailog2, that was
|
||
developed for the Independent Choice Logic, or SLDNFAD, an algorithm based on
|
||
SLDNF. However, both of these algorithms run the risk of going into infinite
|
||
loops and of performing redundant computations.
|
||
|
||
In order to avoid these problems, we present SLGAD resolution that computes
|
||
the (conditional) probability of a ground query from a range\--restricted LPAD
|
||
and is based on SLG resolution for normal logic programs. As SLG, it uses
|
||
tabling to avoid some infinite loops and to avoid redundant computations.
|
||
|
||
The performances of SLGAD are evaluated on classical benchmarks for normal logic
|
||
programs under the well\--founded semantics, namely a 2\--person game and the
|
||
ancestor relation, and on a game of dice.
|
||
|
||
SLGAD is compared with Ailog2 and SLDNFAD on the problems in which they do
|
||
not go into infinite loops, namely those that are described by a modularly
|
||
acyclic program.
|
||
|
||
On the 2\--person game and the ancestor relation, SLGAD is more expensive than
|
||
SLDNFAD on problems where SLDNFAD succeeds but is faster than Ailog2 when the
|
||
query is true in an exponential number of instances.
|
||
|
||
If the program requires the repeated computation of similar goals, as for the
|
||
dice game, then SLGAD outperforms both Ailog2 and SLDNFAD.},
|
||
}
|
||
year={2009},
|
||
year={in press},
|
||
@inproceedings{Rig09-RCRA-IW,
|
||
author={F. Riguzzi},
|
||
title={The {SLGAD} Procedure for Inference on {Logic Programs with Annotated
|
||
Disjunctions}},
|
||
booktitle={{RCRA} workshop on Experimental Evaluation
|
||
of Algorithms for Solving Problems with Combinatorial Explosion},
|
||
editor={Marco Gavanelli and Toni Mancini},
|
||
url={http://ceur-ws.org/Vol-451/paper15riguzzi.pdf},
|
||
series={CEUR Workshop Proceedings},
|
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publisher={Sun {SITE} Central Europe},
|
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issn={1613-0073},
|
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number={451},
|
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year={2009},
|
||
}
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|
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}
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|
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|
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|
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This file was created with JabRef 2.2.
|
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Encoding: Cp1252
|
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@article{DBLP:journals/ai/Poole97,
|
||
author = {David Poole},
|
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|
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|
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}
|
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@article{DBLP:journals/ai/Poole93,
|
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|
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title = {The Independent Choice Logic and Beyond},
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}
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year = {2006},
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}
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@ARTICLE{DBLP:journals/ai/Halpern90,
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@INPROCEEDINGS{DBLP:conf/ki/HitzlerW02,
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}
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@INPROCEEDINGS{DBLP:conf/mlg/JaegerLM07,
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author = {Manfred Jaeger and Petr Lidman and Juan L. Mateo},
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title = {Mining and Learning with Graphs, MLG 2007, Firence, Italy, August
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1-3, 2007, Proceedings},
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year = {2007},
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editor = {Paolo Frasconi and Kristian Kersting and Koji Tsuda}
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}
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@INPROCEEDINGS{DBLP:conf/ecml/KerstingG04,
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title = {Fisher Kernels for Logical Sequences.},
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year = {2004},
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pages = {205-216},
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publisher = {Springer},
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bibsource = {DBLP, http://dblp.uni-trier.de},
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ee = {http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3201{\&}spage=205}
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}
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title = {Logical Hidden Markov Models.},
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journal = {Journal of Artificial Intelligence Research},
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year = {2006},
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volume = {25},
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pages = {425-456}
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}
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@INPROCEEDINGS{DBLP:conf/psb/KerstingRKR03,
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author = {K. Kersting and T. Raiko and S. Kramer and L. De Raedt},
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title = {Towards Discovering Structural Signatures of Protein Folds Based
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on Logical Hidden Markov Models.},
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year = {2003},
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pages = {192-203},
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bibsource = {DBLP, http://dblp.uni-trier.de},
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ee = {http://helix-web.stanford.edu/psb03/kersting.pdf}
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}
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@INPROCEEDINGS{DBLP:conf/ilp/Koller99,
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author = {Daphne Koller},
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title = {Probabilistic Relational Models},
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booktitle = {Proceedings of the 9th International Workshop on Inductive Logic Programming},
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}
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@ARTICLE{LauSpi-JRS88,
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author = {Lauritzen, S. and Spiegelhalter, D. J.},
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title = {Local Computations with Probabilities on Graphical Structures and
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Their Application to Expert Systems},
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}
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@INPROCEEDINGS{DBLP:conf/icml/LuG03,
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author = {Qing Lu and Lise Getoor},
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title = {Link-based Classification},
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booktitle = {International Conference
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on Machine Learning},
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year = {2003},
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publisher = {AAAI Press}
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}
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@ARTICLE{DBLP:journals/datamine/MannilaT97,
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}
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@INPROCEEDINGS{MilDre-ISML02-IC,
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author = {Miller, D. M. and Drechsler, R.},
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title = {On the construction of multiple-valued decision diagrams},
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Logic},
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}
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@ARTICLE{DBLP:journals/etai/Muggleton00,
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title = {Learning Stochastic Logic Programs},
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}
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@ARTICLE{DBLP:journals/ngc/Poole93,
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author = {David Poole},
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title = {Logic Programming, Abduction and Probability - A Top-Down Anytime
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Algorithm for Estimating Prior and Posterior Probabilities},
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}
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@INCOLLECTION{Prz88-Chapter,
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author = {T. C. Przymusinski},
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title = {On the declarative semantics of deductive databases and logic programs},
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}
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@ARTICLE{DBLP:journals/ml/RichardsonD06,
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author = {Matthew Richardson and Pedro Domingos},
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title = {Markov logic networks},
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journal = {Machine Learning},
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ee = {http://dx.doi.org/10.1007/s10994-006-5833-1}
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}
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@INPROCEEDINGS{Rig-AIIA07-IC,
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author = { Fabrizio Riguzzi },
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title = {A Top Down Interpreter for {LPAD} and {CP}\--logic},
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booktitle = {Congress of the Italian Association for Artificial
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Intelligence},
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year = {2007},
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volume = {4733},
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series = {LNAI},
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pages = {109--120},
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publisher = {Springer},
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abstract = {Logic Programs with Annotated Disjunctions and CP-logic are two different
|
||
but related languages for expressing probabilistic information in
|
||
logic programming. The paper presents a top down interpreter for
|
||
computing the probability of a query from a program in one of these
|
||
two languages. The algorithm is based on the one available for ProbLog.
|
||
The performances of the algorithm are compared with those of a Bayesian
|
||
reasoner and with those of the ProbLog interpreter. On programs that
|
||
have a small grounding, the Bayesian reasoner is more scalable, but
|
||
programs with a large grounding require the top down interpreter.
|
||
The comparison with ProbLog shows that the added expressiveness effectively
|
||
requires more computation resources.},
|
||
keywords = {Probabilistic Logic Programming, Logic Programs with Annotated Disjunction,
|
||
Probabilistic Reasoning},
|
||
pdf = {http://www.ing.unife.it/docenti/FabrizioRiguzzi/Papers/Rig-AIIA07.pdf},
|
||
doi = {10.1007/978-3-540-74782-6\string_11 },
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||
|
||
}
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url = {http://www.springerlink.com/content/v7m1k21607xhh365/},
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@INPROCEEDINGS{Rig-ILP06,
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author = {F. Riguzzi},
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title = {{ALLPAD}: Approximate Learning of Logic Programs with Annotated Disjunctions},
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booktitle = {Proceedings of the 16th International Conference on Inductive Logic
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Programming},
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year = {2007},
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number = {4455},
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series = {LNAI},
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publisher = {Springer}
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||
}
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||
@TECHREPORT{Rig06-TR,
|
||
author = {F. Riguzzi},
|
||
title = {{ALLPAD}: Approximate Learning of Logic Programs With Annotated Disjunctions},
|
||
institution = {University of Ferrara},
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||
year = {2006},
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||
number = {CS-2006-01},
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note = {http://www.ing.unife.it/aree\_ricerca/informazione/cs/technical\_reports/CS-2006-01.pdf}
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||
}
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@INPROCEEDINGS{Rig04-ILP04-IC-short,
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author = {F. Riguzzi},
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title = {Learning Logic Programs with Annotated Disjunctions},
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booktitle = {Inductive Logic Programming, ({ILP} 2004)},
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year = {2004},
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number = {3194},
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series = {LNCS},
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pages = {270--287},
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month = {September},
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publisher = {Springer},
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doi = {10.1007/b10011},
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isbn = {3-540-22941-8},
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issn = {0302-9743},
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url = {http://www.ing.unife.it/docenti/FabrizioRiguzzi/Papers/Rig-ILP04.pdf}
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}
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location = {Denver, Colorado, United States}
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}
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author = {Petteri Sevon and Lauri Eronen and Petteri Hintsanen and Kimmo Kulovesi
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}
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% TLS: took address and publisher out for space.
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author = {M. Turcotte and S. Muggleton and M. J. E. Sternberg},
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title = {The Effect of Relational Background Knowledge on Learning of Protein
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Three-Dimensional Fold Signatures.},
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journal = {Machine Learning},
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bibsource = {DBLP, http://dblp.uni-trier.de}
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}
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@ARTICLE{well-founded,
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author = {Van Gelder, A. and K. A. Ross and J. S. Schlipf},
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title = {The Well-founded Semantics for General Logic Programs},
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year = {1991},
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volume = {38},
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@INPROCEEDINGS{VenDenBru-JELIA06,
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author = {J. Vennekens and M. Denecker and M. Bruynooghe},
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title = {Representing Causal Information about a Probabilistic Process},
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@ARTICLE{DBLP:journals/jair/ZhangP96,
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@PROCEEDINGS{DBLP:conf/ilp/2008p,
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title = {Probabilistic Inductive Logic Programming - Theory and Applications},
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year = {2008},
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editor = { De Raedt, Luc and Paolo Frasconi and Kristian Kersting and Stephen
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Muggleton},
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volume = {4911},
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series = {LNCS},
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publisher = {Springer},
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bibsource = {DBLP, http://dblp.uni-trier.de},
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booktitle = {Probabilistic Inductive Logic Programming},
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isbn = {978-3-540-78651-1}
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}
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@inproceedings{VG89,
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AUTHOR = "A. {van Gelder}",
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TITLE = "The Alternating Fixpoint of Logic Programs with Negation",
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BOOKTITLE = "Symposium
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ORGANIZATION = "ACM Press",
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Note = "To Appear",
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Year = 2011}
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@Book{Lloyd,
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author = {J. W. Lloyd},
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title = {Foundations of Logic Programming},
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