PLINI: A Probabilistic Logic Program Framework for Inconsistent News Information

2011 ◽  
pp. 347-376 ◽  
Author(s):  
Massimiliano Albanese ◽  
Matthias Broecheler ◽  
John Grant ◽  
Maria Vanina Martinez ◽  
V. S. Subrahmanian
Keyword(s):  
Logic Program ◽  
Probabilistic Logic

scholarly journals Probabilistic logic reasoning for subjective interestingness analysis

2019 ◽  
Vol 11 (1) ◽  
pp. 59-66
Author(s):  
José Carlos Ferreira Da Rocha ◽  
Alaine M. Guimarães ◽  
Valter L. Estevam Jr.
Keyword(s):  
Linear Programming ◽  
Domain Knowledge ◽  
Logic Program ◽  
Probabilistic Logic ◽  
Classification Rules ◽  
Subjective Interestingness ◽  
Sample Application ◽  
Logic Reasoning

This paper presents an approach that uses probabilistic logic reasoning to compute subjective interestingness scores for classification rules. In the proposed approach, domain knowledge is represented as a probabilistic logic program that encodes information from experts and statistical reports. The computation of interestingness scores is performed by a procedure that applies linear programming to reasoning regarding the probabilities of interest. It provides a mechanism to calculate probability-based subjective interestingness scores. Further, a sample application illustrates the use of the described approach.

A Probabilistic Logic Program that Predicts COVID-19 Infection and the Possibility a Patient to Recover

2020 ◽  
Author(s):  
Nikos Papatheodorou ◽  
Nikos Ntantinakis ◽  
Stefanos Zervoudakis ◽  
Emmanouil Marakakis ◽  
Haridimos Kondylakis
Keyword(s):  
Logic Program ◽  
Probabilistic Logic

scholarly journals An Asymptotic Analysis of Probabilistic Logic Programming, with Implications for Expressing Projective Families of Distributions

2021 ◽  
pp. 1-16
Author(s):  
FELIX Q. WEITKÄMPER
Keyword(s):  
Logic Programming ◽  
Size Dependence ◽  
Logic Program ◽  
Domain Size ◽  
Probabilistic Logic ◽  
Logic Programs ◽  
Probabilistic Logic Programming ◽  
Lifted Inference ◽  
Relational Domains ◽  
Distribution Semantics

Abstract Probabilistic logic programming is a major part of statistical relational artificial intelligence, where approaches from logic and probability are brought together to reason about and learn from relational domains in a setting of uncertainty. However, the behaviour of statistical relational representations across variable domain sizes is complex, and scaling inference and learning to large domains remains a significant challenge. In recent years, connections have emerged between domain size dependence, lifted inference and learning from sampled subpopulations. The asymptotic behaviour of statistical relational representations has come under scrutiny, and projectivity was investigated as the strongest form of domain size dependence, in which query marginals are completely independent of the domain size. In this contribution we show that every probabilistic logic program under the distribution semantics is asymptotically equivalent to an acyclic probabilistic logic program consisting only of determinate clauses over probabilistic facts. We conclude that every probabilistic logic program inducing a projective family of distributions is in fact everywhere equivalent to a program from this fragment, and we investigate the consequences for the projective families of distributions expressible by probabilistic logic programs.

scholarly journals Inference and learning in probabilistic logic programs using weighted Boolean formulas

2014 ◽  
Vol 15 (3) ◽  
pp. 358-401 ◽  
Author(s):  
DAAN FIERENS ◽  
GUY VAN DEN BROECK ◽  
JORIS RENKENS ◽  
DIMITAR SHTERIONOV ◽  
BERND GUTMANN ◽  
...  
Keyword(s):  
Graphical Model ◽  
State Of The Art ◽  
Logic Program ◽  
Knowledge Compilation ◽  
Boolean Formula ◽  
Probabilistic Logic ◽  
Logic Programs ◽  
Inference Algorithms ◽  
Boolean Formulas ◽  
Model Counting

AbstractProbabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks, such as computing the marginals, given evidence and learning from (partial) interpretations, have not really been addressed for probabilistic logic programs before. The first contribution of this paper is a suite of efficient algorithms for various inference tasks. It is based on the conversion of the program and the queries and evidence to a weighted Boolean formula. This allows us to reduce inference tasks to well-studied tasks, such as weighted model counting, which can be solved using state-of-the-art methods known from the graphical model and knowledge compilation literature. The second contribution is an algorithm for parameter estimation in the learning from interpretations setting. The algorithm employs expectation-maximization, and is built on top of the developed inference algorithms. The proposed approach is experimentally evaluated. The results show that the inference algorithms improve upon the state of the art in probabilistic logic programming, and that it is indeed possible to learn the parameters of a probabilistic logic program from interpretations.

Stable and Supported Semantics in Continuous Vector Spaces

2020 ◽  
Author(s):  
Yaniv Aspis ◽  
Krysia Broda ◽  
Alessandra Russo ◽  
Jorge Lobo
Keyword(s):  
Logic Program ◽  
Matrix Multiplication ◽  
Vector Spaces ◽  
Continuous Space ◽  
Continuous Vector ◽  
Novel Approach ◽  
Gradient Based ◽  
Normal Logic ◽  
Parameter Values ◽  
Normal Logic Program

We introduce a novel approach for the computation of stable and supported models of normal logic programs in continuous vector spaces by a gradient-based search method. Specifically, the application of the immediate consequence operator of a program reduct can be computed in a vector space. To do this, Herbrand interpretations of a propositional program are embedded as 0-1 vectors in $\mathbb{R}^N$ and program reducts are represented as matrices in $\mathbb{R}^{N \times N}$. Using these representations we prove that the underlying semantics of a normal logic program is captured through matrix multiplication and a differentiable operation. As supported and stable models of a normal logic program can now be seen as fixed points in a continuous space, non-monotonic deduction can be performed using an optimisation process such as Newton's method. We report the results of several experiments using synthetically generated programs that demonstrate the feasibility of the approach and highlight how different parameter values can affect the behaviour of the system.

A Dynamic Approach on Quickly Predicting Non-Termination of Logic Program of Security Protocol

2011 ◽  
Vol 34 (7) ◽  
pp. 1275-1283
Author(s):  
Ti ZHOU ◽  
Meng-Jun LI ◽  
Zhou-Jun LI
Keyword(s):  
Logic Program ◽  
Security Protocol ◽  
Dynamic Approach

Modeling Genetic Regulatory Networks Using First-Order Probabilistic Logic

2013 ◽  
Author(s):  
Nand Kishore ◽  
Radhakrishnan Balu ◽  
Shashi P. Karna
Keyword(s):  
Regulatory Networks ◽  
Genetic Regulatory Networks ◽  
Probabilistic Logic ◽  
First Order

Y-Logic: A Framework for Reasoning About Chameleonic Programs with Inconsistent Completions

1990 ◽  
Vol 13 (4) ◽  
pp. 465-483
Author(s):  
V.S. Subrahmanian
Keyword(s):  
Classical Logic ◽  
Logic Program ◽  
Logic Programs ◽  
Large Subset ◽  
General Logic ◽  
Traditional Sense

Large logic programs are normally designed by teams of individuals, each of whom designs a subprogram. While each of these subprograms may have consistent completions, the logic program obtained by taking the union of these subprograms may not. However, the resulting program still serves a useful purpose, for a (possibly) very large subset of it still has a consistent completion. We argue that “small” inconsistencies may cause a logic program to have no models (in the traditional sense), even though it still serves some useful purpose. A semantics is developed in this paper for general logic programs which ascribes a very reasonable meaning to general logic programs irrespective of whether they have consistent (in the classical logic sense) completions.

scholarly journals Special issue on ‘Program development’

2002 ◽  
Vol 2 (4-5) ◽  
pp. 423-424 ◽  
Author(s):  
MAURICE BRUYNOOGHE ◽  
KUNG-KIU LAU
Keyword(s):  
Software Engineering ◽  
Program Development ◽  
Real World ◽  
Development Process ◽  
Logic Program ◽  
Future Challenge ◽  
Special Issue ◽  
Logical Foundations ◽  
The Past ◽  
Specification Synthesis

This special issue marks the tenth anniversary of the LOPSTR workshop. LOPSTR started in 1991 as a workshop on Logic Program Synthesis and Transformation, but later it broadened its scope to logic-based Program Development in general.The motivating force behind LOPSTR has been a belief that declarative paradigms such as logic programming are better suited to program development tasks than traditional non-declarative ones such as the imperative paradigm. Specification, synthesis, transformation or specialisation, analysis, verification and debugging can all be given logical foundations, thus providing a unifying framework for the whole development process.In the past ten years or so, such a theoretical framework has indeed begun to emerge. Even tools have been implemented for analysis, verification and specialisation. However, it is fair to say that so far the focus has largely been on programming-in-the-small. So the future challenge is to apply or extend these techniques to programming-in-the-large, in order to tackle software engineering in the real world.

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