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On the Implementation of the Probabilistic Logic Programming Language ProbLog
 UNDER CONSIDERATION FOR PUBLICATION IN THEORY AND PRACTICE OF LOGIC PROGRAMMING
, 2003
"... The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have been developed. ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networ ..."
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Cited by 41 (9 self)
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The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have been developed. ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological
ProbLog2: From Probabilistic Programming to Statistical Relational Learning
"... ProbLog is a probabilistic programming language based on Prolog. The new ProbLog system called ProbLog2 can solve a range of inference and learning tasks typical for the Probabilistic Graphical Models (PGM) and Statistical Relational Learning (SRL) communities. The main mechanism behind ProbLog2 ..."
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ProbLog is a probabilistic programming language based on Prolog. The new ProbLog system called ProbLog2 can solve a range of inference and learning tasks typical for the Probabilistic Graphical Models (PGM) and Statistical Relational Learning (SRL) communities. The main mechanism behind ProbLog2
Learning probabilistic relational models
 In IJCAI
, 1999
"... A large portion of realworld data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much ..."
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Cited by 619 (31 self)
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of the relational structure present in our database. This paper builds on the recent work on probabilistic relational models (PRMs), and describes how to learn them from databases. PRMs allow the properties of an object to depend probabilistically both on other properties of that object and on properties of related
Abduction in Logic Programming
"... Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over th ..."
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Cited by 616 (76 self)
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Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over
ProbLog: a probabilistic Prolog and its application in link discovery
 In Proceedings of 20th International Joint Conference on Artificial Intelligence
, 2007
"... We introduce ProbLog, a probabilistic extension of Prolog. A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program, and these probabilities are mutually independent. The semantics of ProbLog is then defi ..."
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Cited by 146 (27 self)
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We introduce ProbLog, a probabilistic extension of Prolog. A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program, and these probabilities are mutually independent. The semantics of ProbLog
ProbLog: A
 Probabilistic Prolog and Its Application in Link Discovery”. In IJCAI
, 2007
"... ProbLog [1] is a probabilistic programming language that extends Prolog along the lines of Sato’s distribution semantics. Its development focusses especially on machine learning techniques and implementation aspects. The ProbLog implementation is publicly available as part of Yap Prolog at ..."
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Cited by 1 (0 self)
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ProbLog [1] is a probabilistic programming language that extends Prolog along the lines of Sato’s distribution semantics. Its development focusses especially on machine learning techniques and implementation aspects. The ProbLog implementation is publicly available as part of Yap Prolog at
Temporal and modal logic
 HANDBOOK OF THEORETICAL COMPUTER SCIENCE
, 1995
"... We give a comprehensive and unifying survey of the theoretical aspects of Temporal and modal logic. ..."
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Cited by 1300 (17 self)
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We give a comprehensive and unifying survey of the theoretical aspects of Temporal and modal logic.
Variable Compression in ProbLog
, 2010
"... The paper identifies patterns of Boolean variables that occur in Boolean formulae, namely ANDclusters and ORclusters. We give a polynomial algorithm that detects ANDclusters in disjunctive normal form (DNF) Boolean formulae, or ORclusters in conjunctive normal form (CNF) Boolean formulae. Furthe ..."
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. Furthermore, we explain how to exploit the clusters in the context of ProbLog. In ProbLog, Boolean formulae are used to express how the probability of a query depends on the probabilistic part of a ProbLog program. Boolean formulae in ProbLog are represented by Reduced Ordered Binary Decision Diagrams (ROBDD
Markov Logic Networks
 MACHINE LEARNING
, 2006
"... We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the ..."
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Cited by 811 (39 self)
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We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects
Results 1  10
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413,068