cplint on SWISH
is a web application for trying
probabilistic logic programming
with a Javascriptenabled web browser on any operating system. It was written by
Fabrizio Riguzzi, Riccardo Zese and Giuseppe Cota.
Please use this forum for questions or send an email to cplint@googlegroups.com.
SWISH was originally written by Torbjörn Lager
as a homage to SWIProlog. Jan Wielemaker designed and implemented the
present version. The current SWISH
application targets primarily at collaborative exploration of data. SWISH
can be combined with e.g., CQL to explore
relational (SQL) databases or sparkle to
explore linked data. A ClioPatria
plugin adds Prolog based exploration of RDF data to ClioPatria.
SWISH is a great tool for teaching Prolog.
We provide a prototype of
Learn Prolog Now! where SWISH is embedded to run examples and solve
excercises from within your browser.
The cplint on
SWISH source is available from
Github. It
requires SWIProlog 7 installed from the
latest GIT.
The cplint
source is available from
Github.
The SWISH source is available from Github.
SWISH is described in:

Jan Wielemaker, Torbjörn Lager, and Fabrizio Riguzzi.
SWISH: SWIProlog for sharing.
In Stefan Ellmauthaler and Claudia Schulz, editors,
International Workshop on UserOriented Logic Programming (IULP 2015),
© by the authors, 2015.
[ bib 
http ]
cplint on SWISH is described in:

Fabrizio Riguzzi, Elena Bellodi, Evelina Lamma, Riccardo Zese, and Giuseppe
Cota.
Probabilistic logic programming on the web.
Software: Practice and Experience, © Wiley, 2015.
[ bib 
DOI 
.pdf ]
The algorithm for exact probabilistic inference (PITA) is described in:

Fabrizio Riguzzi and Terrance Swift.
Welldefinedness and efficient inference for probabilistic logic
programming under the distribution semantics.
Theory and Practice of Logic Programming, 13(Special Issue 02 
25th Annual GULP Conference):279302, © Cambridge University
Press, March 2013.
[ bib 
DOI 
.pdf ]

Fabrizio Riguzzi and Terrance Swift.
The PITA system: Tabling and answer subsumption for reasoning under
uncertainty.
Theory and Practice of Logic Programming, 27th International
Conference on Logic Programming (ICLP'11) Special Issue, Lexington, Kentucky
610 July 2011, 11(45):433449, © Cambridge University
Press, 2011.
[ bib 
DOI 
.pdf ]

Fabrizio Riguzzi and Terrance Swift.
Tabling and Answer Subsumption for Reasoning on Logic
Programs with Annotated Disjunctions.
In M. Hermenegildo and T. Schaub, editors, Technical
Communications of the 26th Int'l. Conference on Logic Programming (ICLP'10),
volume 7 of Leibniz International Proceedings in Informatics (LIPIcs),
pages 162171, Dagstuhl, Germany, July 2010. License Creative Commons
AttributionNoncommercialNo Derivative Works 3.0, Schloss
DagstuhlLeibnizZentrum fuer Informatik.
[ bib 
DOI 
http ]
The algorithm for Monte Carlo inference (MCINTYRE) is described in:

Fabrizio Riguzzi.
MCINTYRE: A Monte Carlo system for probabilistic logic
programming.
Fundamenta Informaticae, 124(4):521541, © IOS
Press, 2013.
[ bib 
DOI 
.pdf ]
The algorithm for Metropolis/Hastings sampling is described in:

Arun Nampally and C. R. Ramakrishnan.
Adaptive MCMCBased Inference in Probabilistic Logic Programs.
arXiv preprint arXiv:1403.6036, 2014.
[ .pdf ]
The algorithm for parameter learning (EMBLEM) is described in:
 Elena Bellodi and Fabrizio Riguzzi.
Expectation Maximization over binary decision diagrams for
probabilistic logic programs.
Intelligent Data Analysis, 17(2):343363, © IOS
Press, 2013.
[ bib 
DOI 
http 
.pdf ]

Elena Bellodi and Fabrizio Riguzzi.
Experimentation of an expectation maximization algorithm for
probabilistic logic programs.
Intelligenza Artificiale, 8(1):318, © IOS
Press, 2012.
[ bib 
DOI 
.pdf ]
The SLIPCOVER algorithm for structure learning is described in:

Elena Bellodi and Fabrizio Riguzzi.
Structure learning of probabilistic logic programs by searching the
clause space.
Theory and Practice of Logic Programming, 15(2):169212,
© Cambridge University Press, 2015.
[ bib 
DOI 
http 
http ]
The LEMUR algorithm for structure learning is described in:

Nicola Di Mauro, Elena Bellodi, and Fabrizio Riguzzi.
Banditbased MonteCarlo structure learning of probabilistic logic programs.
Machine Learning, 100(1):127156, © Springer International Publishing, July 2015.
[ bib 
DOI 
pdf ]