Add to ORKGThe relations between ProbLog and Logic Programs with Annotated Disjunctions imply that Boolean Bayesian networks can be represented as ground ProbLog programs and acyclic ground ProbLog programs can be represented as Boolean Bayesian networks. This provides a way of learning ground acyclic ProbLog programs from interpretations: first the interpretations are represented in tabular form, then a Bayesian network learning algorithm is applied and the learned network is translated into a ground ProbLog program. The program is then further analyzed in order to identify noisy or relations in it. The paper proposes an algorithm for such identification and presents an experimental analysis of its computational complexity
We introduce ProbLog, a probabilistic extension of Prolog. A ProbLog program defines a distribution ...
An issue that has so far received only limited attention in probabilistic logic programming (PLP) is...
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...
ProbLog is a recently introduced probabilistic extension of the logic programming language Prolog, i...
The past few years have seen a surge of interest in the field of probabilistic logic learning and st...
The past few years have seen a surge of interest in the field of probabilistic logic learning and ...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
Abstract. Causal relationships are present in many application domains. CP-logic is a probabilistic ...
Causal relationships are present in many application domains. CP-logic is a probabilistic modeling l...
The ability to reason about large numbers of objects, their attributes, and relationships between th...
Probabilistic logic programs are logic programs in which some of the facts are annotated with probab...
Bayesian Logic Programs (BLP) [8][9] is a powerful and elegant framework for combining the expressiv...
Several models combining Bayesian networks with logic exist. The two most developed models are Proba...
Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a...
© Springer-Verlag Berlin Heidelberg 2001. Recently, new representation languages that integrate firs...
We introduce ProbLog, a probabilistic extension of Prolog. A ProbLog program defines a distribution ...
An issue that has so far received only limited attention in probabilistic logic programming (PLP) is...
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...
ProbLog is a recently introduced probabilistic extension of the logic programming language Prolog, i...
The past few years have seen a surge of interest in the field of probabilistic logic learning and st...
The past few years have seen a surge of interest in the field of probabilistic logic learning and ...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
Abstract. Causal relationships are present in many application domains. CP-logic is a probabilistic ...
Causal relationships are present in many application domains. CP-logic is a probabilistic modeling l...
The ability to reason about large numbers of objects, their attributes, and relationships between th...
Probabilistic logic programs are logic programs in which some of the facts are annotated with probab...
Bayesian Logic Programs (BLP) [8][9] is a powerful and elegant framework for combining the expressiv...
Several models combining Bayesian networks with logic exist. The two most developed models are Proba...
Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a...
© Springer-Verlag Berlin Heidelberg 2001. Recently, new representation languages that integrate firs...
We introduce ProbLog, a probabilistic extension of Prolog. A ProbLog program defines a distribution ...
An issue that has so far received only limited attention in probabilistic logic programming (PLP) is...
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...