@inproceedings{AzzRigLam19-BIS-IW,
author = {Azzolini, Damiano
and Riguzzi, Fabrizio
and Lamma, Evelina},
editor = {Abramowicz, Witold
and Corchuelo, Rafael},
title = {Analyzing Transaction Fees with Probabilistic Logic Programming},
booktitle = {Business Information Systems Workshops BIS 2019},
year = {2019},
publisher = {Springer International Publishing},
address = {Cham},
copyright = {Springer},
pages = {243--254},
abstract = {Fees are used in Bitcoin to prioritize transactions. Transactions with high associated fee are usually included in a block faster than those with lower fees. Users would like to pay just the minimum amount to make the transaction confirmed in the desired time. Fees are collected as a reward when transactions are included in a block so, on the other perspective, miners usually process first the most profitable transactions, i.e. the one with higher fee rate. Bitcoin is a dynamic system influenced by several variables, such as transaction arrival time and block discovery time making the prediction of the confirmation time a hard task. In this paper we use probabilistic logic programming to model how fees influence the confirmation time and how much fees affect miner's revenue.},
isbn = {978-3-030-36691-9},
issn = {1865-1348},
doi = {10.1007/978-3-030-36691-9_21},
series = {Lecture Notes in Business Information Processing},
volume = {373},
url = {http://ml.unife.it/wp-content/uploads/Papers/AzzRigLam-BIS19.pdf},
note = {The final publication is available at Springer via \url{http://dx.doi.org/10.1007/978-3-030-36691-9_21}}
}
@article{Rig19-arXiv-TR,
title = {Quantum Weighted Model Counting},
author = {Fabrizio Riguzzi},
year = {2019},
journal = {arXiv},
volume = {abs/1910.13530},
url = {https://arxiv.org/abs/1910.13530},
primaryclass = {quant-ph}
}
@article{AzzRigLam19-Info-IJ,
author = {Damiano Azzolini and Fabrizio Riguzzi and Evelina Lamma},
title = {Studying Transaction Fees in the Bitcoin Blockchain with Probabilistic Logic Programming},
journal = {Information},
publisher = {MDPI},
copyright = {CCBY},
year = {2019},
pdf = {https://www.mdpi.com/2078-2489/10/11/335/pdf},
doi = {10.3390/info10110335},
abstract = {
In Bitcoin, if a miner is able to solve a computationally hard problem called proof of work, it will receive an amount of bitcoin as a reward which is the sum of the fees for the transactions included in a block plus an amount inversely proportional to the number of blocks discovered so far. At the moment of writing, the block reward is several orders of magnitude greater than the sum of transaction fees. Usually, miners try to collect the largest reward by including transactions associated with high fees. The main purpose of transaction fees is to prevent network spamming. However, they are also used to prioritize transactions. In order to use the minimum amount of fees, users usually have to find a compromise between fees and urgency of a transaction. In this paper, we develop a probabilistic logic model to experimentally analyze how fees affect confirmation time and miner's revenue and to predict if an increase of average fees will generate a situation when the miner gets more reward by not following the protocol.},
keywords = { bitcoin, blockchain, probabilistic logic programming
},
address = {Basel, Switzerland},
volume = {10},
number = {11},
pages = {335}
}
@inproceedings{AzzRigLamMas19-AIXIA-IC,
title = {A Comparison of {MCMC} Sampling for Probabilistic Logic Programming},
author = {Damiano Azzolini and Fabrizio Riguzzi and Evelina Lamma and Franco Masotti },
booktitle = {Proceedings of the 18th Conference of the Italian Association for Artificial Intelligence ({AI*IA2019}),
Rende, Italy 19-22 November 2019},
year = 2019,
editor = {Mario Alviano and Gianluigi Greco and
Francesco Scarcello},
publisher = {Springer},
address = {Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
venue = {Rende, Italy},
eventdate = {November 19-22, 2019},
copyright = {Springer},
doi = {10.1007/978-3-030-35166-3_2},
volume = {11946},
pages = {18-29},
url = {http://ml.unife.it/wp-content/uploads/Papers/AzzRIgKLamMas-AIXIA19.pdf},
isbn-print = {978-3-030-35165-6},
isbn-online = {978-3-030-35166-3},
note = {The final publication is available at Springer via \url{http://dx.doi.org/10.1007/978-3-030-35166-3_2}}
}
@article{RigKerLipNat19-FAI-EB,
author = {Riguzzi, Fabrizio and Kersting, Kristian and Lippi, Marco and Natarajan, Sriraam},
title = {Editorial: Statistical Relational Artificial Intelligence},
journal = {Frontiers in Robotics and AI},
volume = {6},
pages = {68},
year = {2019},
url = {https://www.frontiersin.org/article/10.3389/frobt.2019.00068},
doi = {10.3389/frobt.2019.00068},
issn = {2296-9144},
copyright = {by the authors},
publisher = {Frontiers Media SA},
address = {Lausanne,
Switzerland}
}
@inproceedings{CotRigZesLam18-CILC-NC,
author = {Cota, Giuseppe and Riguzzi, Fabrizio and Lamma, Evelina and Zese, Riccardo},
title = {{KRaider}: a Crawler for Linked Data},
year = {2019},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
address = {Aachen, Germany},
volume = {2396},
editor = {Alberto Casagrande and Eugenio Omodeo},
booktitle = {Proceedings of the 34th Italian Conference on Computational Logic},
url = {http://ceur-ws.org/Vol-2396/paper35.pdf},
pages = {202-216},
eventdate = {June 19-21, 2019},
venue = {Trieste, Italy},
issn = {1613-0073},
copyright = {by the authors},
scopus = {2-s2.0-85071120572}
}
@article{NguRig19-ML-IJ,
author = {Nguembang Fadja, Arnaud and Fabrizio Riguzzi},
title = {Lifted Discriminative Learning of Probabilistic Logic Programs},
journal = {Machine Learning},
publisher = {Springer},
copyright = {Springer},
year = {2019},
doi = {10.1007/s10994-018-5750-0},
abstract = {
Probabilistic logic programming (PLP) provides a powerful tool for reason- ing with uncertain relational models. However, learning probabilistic logic programs is expensive due to the high cost of inference. Among the proposals to overcome this problem, one of the most promising is lifted inference. In this paper we consider PLP models that are amenable to lifted inference and present an algorithm for performing parameter and structure learning of these models from positive and negative exam- ples. We discuss parameter learning with EM and LBFGS and structure learning with LIFTCOVER, an algorithm similar to SLIPCOVER. The results of the comparison of LIFTCOVER with SLIPCOVER on 12 datasets show that it can achieve solutions of similar or better quality in a fraction of the time.
},
keywords = { Statistical Relational Learning, Probabilistic Inductive Logic Program- ming, Probabilistic Logic Programming, Lifted Inference, Expectation Maximization
},
scopus = {2-s2.0-85052570852},
volume = {108},
number = {7},
pages = {1111--1135}
}
@article{LacVraRig19-ML-EB,
author = {Lachiche, Nicolas and Vrain, Christel and Riguzzi, Fabrizio and Bellodi Elena and Zese Riccardo},
title = {Preface to special issue on {Inductive Logic Programming}, {ILP} 2017 and 2018},
journal = {Machine Learning},
year = {2019},
pages = {1-3},
copyrigth = {Springer},
publisher = {Springer US},
doi = {10.1007/s10994-019-05790-6},
scopus = {2-s2.0-85061585564}
}
@article{ZesBelCot19-TPLP-IJ,
title = {Probabilistic {DL} Reasoning with Pinpointing Formulas: A Prolog-based Approach},
doi = {10.1017/S1471068418000480},
journal = {Theory and Practice of Logic Programming},
publisher = {Cambridge University Press},
copyright = {Cambridge University Press},
author = {Zese, Riccardo and Cota, Giuseppe and Lamma, Evelina and Bellodi, Elena and Riguzzi, Fabrizio},
pages = {449--476},
year = {2019},
volume = {19},
number = {3},
pdf = {https://arxiv.org/pdf/1809.06180.pdf},
scopus = {2-s2.0-85060024345},
doi = {10.1017/S1471068418000480}
}
@article{Rig19-MNa-RE,
author = {Fabrizio Riguzzi},
title = {Review of
{Avanzini, Martin; Dal Lago, Ugo. On sharing, memoization, and polynomial time. Inform. and Comput. 261 (2018), part 1, 3--22.}},
journal = {Mathematical Reviews},
publisher = {American Mathematical Society},
copyright = {American Mathematical Society},
year = {2019},
month = {January},
issn = {2167-5163},
mrnumber = {MR3812325},
mrreviewer = {Fabrizio Riguzzi},
url = {http://www.ams.org/mathscinet-getitem?mr=3812325 }
}
@article{WieRigKow19-TPLP-IJ,
author = {
Jan Wielemaker and Fabrizio Riguzzi and Bob Kowalski and Torbj\"orn Lager and Fariba Sadri and Miguel Calejo },
title = {Using {SWISH} to realise interactive web based tutorials for logic based languages },
journal = {Theory and Practice of Logic Programming},
year = {2019},
volume = {19},
doi = {10.1017/S1471068418000522},
number = {2},
publisher = {Cambridge University Press},
copyright = {Cambridge University Press},
pages = {229-261},
pdf = {https://arxiv.org/pdf/1808.08042.pdf},
abstrac = {Programming environments have evolved from purely text based to using graphical user interfaces,
and now we see a move toward web-based interfaces, such as Jupyter. Web-based interfaces allow for the
creation of interactive documents that consist of text and programs, as well as their output. The output
can be rendered using web technology as, for example, text, tables, charts, or graphs. This approach
is particularly suitable for capturing data analysis workflows and creating interactive educational
material. This article describes SWISH, a web front-end for Prolog that consists of a web server
implemented in SWI-Prolog and a client web application written in JavaScript. SWISH provides a
web server where multiple users can manipulate and run the same material, and it can be adapted
to support Prolog extensions. In this article we describe the architecture of SWISH, and describe
two case studies of extensions of Prolog, namely Probabilistic Logic Programming and Logic Production
System, which have used SWISH to provide tutorial sites.},
keywords = {Prolog, logic programming system, notebook interface, web},
scopus = {2-s2.0-85061599946}
}
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