scholarly journals ProbLog2: Probabilistic Logic Programming

2015 ◽  
pp. 312-315 ◽  
Author(s):  
Anton Dries ◽  
Angelika Kimmig ◽  
Wannes Meert ◽  
Joris Renkens ◽  
Guy Van den Broeck ◽  
...  
Keyword(s):  
Logic Programming ◽  
Probabilistic Logic ◽  
Probabilistic Logic Programming

Abduction with Probabilistic Logic Programming under the Distribution Semantics

2021 ◽  
Author(s):  
Damiano Azzolini ◽  
Elena Bellodi ◽  
Stefano Ferilli ◽  
Fabrizio Riguzzi ◽  
Riccardo Zese
Keyword(s):  
Logic Programming ◽  
Probabilistic Logic ◽  
Probabilistic Logic Programming ◽  
Distribution Semantics

Probabilistic Logic Programming (PLP 2017)

2019 ◽  
Vol 106 ◽  
pp. 88
Author(s):  
Christian Theil Have ◽  
Riccardo Zese
Keyword(s):  
Logic Programming ◽  
Probabilistic Logic ◽  
Probabilistic Logic Programming

scholarly journals MAP Inference for Probabilistic Logic Programming

2020 ◽  
Vol 20 (5) ◽  
pp. 641-655
Author(s):  
ELENA BELLODI ◽  
MARCO ALBERTI ◽  
FABRIZIO RIGUZZI ◽  
RICCARDO ZESE
Keyword(s):  
Logic Programming ◽  
Maximum A Posteriori ◽  
Probabilistic Logic ◽  
A Posteriori ◽  
Binary Decision ◽  
Map Inference ◽  
Probabilistic Logic Programming ◽  
Synthetic Datasets ◽  
Most Probable Explanation ◽  
Inference Task

AbstractIn Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the marginal probability of a query given a program. In this paper, we consider two other important tasks in the PLP setting: the Maximum-A-Posteriori (MAP) inference task, which determines the most likely values for a subset of the random variables given evidence on other variables, and the Most Probable Explanation (MPE) task, the instance of MAP where the query variables are the complement of the evidence variables. We present a novel algorithm, included in the PITA reasoner, which tackles these tasks by representing each problem as a Binary Decision Diagram and applying a dynamic programming procedure on it. We compare our algorithm with the version of ProbLog that admits annotated disjunctions and can perform MAP and MPE inference. Experiments on several synthetic datasets show that PITA outperforms ProbLog in many cases.

scholarly journals Probabilistic logic programming

1992 ◽  
Vol 101 (2) ◽  
pp. 150-201 ◽  
Author(s):  
Raymond Ng ◽  
V.S. Subrahmanian
Keyword(s):  
Logic Programming ◽  
Probabilistic Logic ◽  
Probabilistic Logic Programming

scholarly journals Combining probabilistic logic programming with the power of maximum entropy

2004 ◽  
Vol 157 (1-2) ◽  
pp. 139-202 ◽  
Author(s):  
Gabriele Kern-Isberner ◽  
Thomas Lukasiewicz
Keyword(s):  
Logic Programming ◽  
Maximum Entropy ◽  
Probabilistic Logic ◽  
Probabilistic Logic Programming

scholarly journals A survey of lifted inference approaches for probabilistic logic programming under the distribution semantics

2017 ◽  
Vol 80 ◽  
pp. 313-333 ◽  
Author(s):  
Fabrizio Riguzzi ◽  
Elena Bellodi ◽  
Riccardo Zese ◽  
Giuseppe Cota ◽  
Evelina Lamma
Keyword(s):  
Logic Programming ◽  
Probabilistic Logic ◽  
Probabilistic Logic Programming ◽  
Lifted Inference ◽  
Distribution Semantics
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