Results 51 to 60 of about 224,550 (312)
Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning
Frontiers in Big Data, 2020 Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks.
Andrea Galassi +4 more
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Probabilistic logic programming
Information and Computation, 1992 The logic programming language for expressing a probabilistic information is proposed. \(P\)-programs are finite sets of clauses of a special kind: the head of a clause is an atomic formula loaded by a closed interval \([a,b]\), and the body is a set of formulae (not only atomic) which are loaded by closed intervals too. The formula \(F:[a,b]\) denotes
Ng, Raymond, Subrahmanian, V.S.
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The Functional Perspective on Advanced Logic Programming [PDF]
, 2016 The basics of logic programming, as embodied by Prolog, are generally well-known in the programming language community. However, more advanced techniques, such as tabling, answer subsumption and probabilistic logic programming fail to attract the ...
Vandenbroucke, Alexander
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Logical Methods in Computer ScienceMeasurable cones, with linear and measurable functions as morphisms, are a model of intuitionistic linear logic and of call-by-name probabilistic PCF which accommodates "continuous data types" such as the real line.
Thomas Ehrhard, Guillaume Geoffroy
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Preferential Cyber Defense for Power Grids
PRX Energy, 2023 The integration of computing and communication capabilities into the power grid has led to vulnerabilities enabling attackers to launch cyberattacks on the grid.
Mohammadamin Moradi +3 more
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Semantic Probabilistic Inference of Predictions
Известия Иркутского государственного университета: Серия "Математика", 2017 Prediction is one of the most important concepts in science. Predictions obtained from probabilistic knowledge, are described by an inductive-statistical inference (I-S inference).
E. E. Vityaev
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Lifted Variable Elimination for Probabilistic Logic Programming
, 2014 Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances.
Bellodi, Elena +4 more
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Stable Model Counting and Its Application in Probabilistic Logic Programming [PDF]
, 2014 Model counting is the problem of computing the number of models that satisfy a given propositional theory. It has recently been applied to solving inference tasks in probabilistic logic programming, where the goal is to compute the probability of given ...
Aziz, Rehan Abdul +3 more
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SkILL - a Stochastic Inductive Logic Learner
, 2015 Probabilistic Inductive Logic Programming (PILP) is a rel- atively unexplored area of Statistical Relational Learning which extends classic Inductive Logic Programming (ILP).
Côrte-Real, Joana +3 more
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Distributional logic programming for Bayesian knowledge representation [PDF]
, 2016 We present a formalism for combining logic programming and its flavour of nondeterminism with probabilistic reasoning. In particular, we focus on representing prior knowledge for Bayesian inference.
Angelopoulos, Nikolaos, Cussens, James
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