Add to ORKGAbstract. We give a logic programming based account of probability and describe a declarative language P-log capable of reasoning which combines both logical and probabilistic arguments. Several non-trivial examples illustrate the use of P-log for knowledge representation.
We present a semantics for Probabilistic Description Logics that is based on the distribution semant...
Recently, there has been a lot of attention for statistical relational learning and probabilistic pr...
We propose a general scheme for adding probabilistic reasoning capabilities to a wide variety of kno...
Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertai...
A multitude of different probabilistic programming languages exists today, all extending a tradition...
AbstractOf all scientific investigations into reasoning with uncertainty and chance, probability the...
Towards sophisticated representation and reasoning techniques that allow for probabilistic uncertain...
A multitude of different probabilistic programming languages exists today, all extending a tradition...
The combination of logic programming and probability has proven useful for modeling domains with com...
Probabilistic programs combine the power of programming languages with that of probabilistic graphic...
Probabilistic programming is an emerging subfield of AI that extends traditional programming languag...
We present a new approach to probabilistic logic programs with a possible worlds semantics. Classica...
We present a probabilistic extension of logic programs under the stable model semantics, inspired by...
A multitude of different probabilistic programming languages exists to-day, all extending a traditio...
Abstract. Two approaches to logic programming with probabilities emerged over time: bayesian reasoni...
We present a semantics for Probabilistic Description Logics that is based on the distribution semant...
Recently, there has been a lot of attention for statistical relational learning and probabilistic pr...
We propose a general scheme for adding probabilistic reasoning capabilities to a wide variety of kno...
Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertai...
A multitude of different probabilistic programming languages exists today, all extending a tradition...
AbstractOf all scientific investigations into reasoning with uncertainty and chance, probability the...
Towards sophisticated representation and reasoning techniques that allow for probabilistic uncertain...
A multitude of different probabilistic programming languages exists today, all extending a tradition...
The combination of logic programming and probability has proven useful for modeling domains with com...
Probabilistic programs combine the power of programming languages with that of probabilistic graphic...
Probabilistic programming is an emerging subfield of AI that extends traditional programming languag...
We present a new approach to probabilistic logic programs with a possible worlds semantics. Classica...
We present a probabilistic extension of logic programs under the stable model semantics, inspired by...
A multitude of different probabilistic programming languages exists to-day, all extending a traditio...
Abstract. Two approaches to logic programming with probabilities emerged over time: bayesian reasoni...
We present a semantics for Probabilistic Description Logics that is based on the distribution semant...
Recently, there has been a lot of attention for statistical relational learning and probabilistic pr...
We propose a general scheme for adding probabilistic reasoning capabilities to a wide variety of kno...