scholarly journals Expectation Maximization in Deep Probabilistic Logic Programming

2018 ◽  
pp. 293-306
Author(s):  
Arnaud Nguembang Fadja ◽  
Fabrizio Riguzzi ◽  
Evelina Lamma
Keyword(s):  
Logic Programming ◽  
Expectation Maximization ◽  
Probabilistic Logic ◽  
Probabilistic Logic Programming

scholarly journals Learning hierarchical probabilistic logic programs

2021 ◽  
Author(s):  
Arnaud Nguembang Fadja ◽  
Fabrizio Riguzzi ◽  
Evelina Lamma
Keyword(s):  
Logic Programming ◽  
Expectation Maximization ◽  
Gradient Descent ◽  
Structure Learning ◽  
Probabilistic Logic ◽  
Logic Programs ◽  
Markov Logic Networks ◽  
Markov Logic ◽  
Probabilistic Logic Programming ◽  
Relational Domains

AbstractProbabilistic logic programming (PLP) combines logic programs and probabilities. Due to its expressiveness and simplicity, it has been considered as a powerful tool for learning and reasoning in relational domains characterized by uncertainty. Still, learning the parameter and the structure of general PLP is computationally expensive due to the inference cost. We have recently proposed a restriction of the general PLP language called hierarchical PLP (HPLP) in which clauses and predicates are hierarchically organized. HPLPs can be converted into arithmetic circuits or deep neural networks and inference is much cheaper than for general PLP. In this paper we present algorithms for learning both the parameters and the structure of HPLPs from data. We first present an algorithm, called parameter learning for hierarchical probabilistic logic programs (PHIL) which performs parameter estimation of HPLPs using gradient descent and expectation maximization. We also propose structure learning of hierarchical probabilistic logic programming (SLEAHP), that learns both the structure and the parameters of HPLPs from data. Experiments were performed comparing PHIL and SLEAHP with PLP and Markov Logic Networks state-of-the art systems for parameter and structure learning respectively. PHIL was compared with EMBLEM, ProbLog2 and Tuffy and SLEAHP with SLIPCOVER, PROBFOIL+, MLB-BC, MLN-BT and RDN-B. The experiments on five well known datasets show that our algorithms achieve similar and often better accuracies but in a shorter time.

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

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

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
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