We show how the amalgamation of Logic Programming with probabilistic reasoning enhances its capabilities for intelligent reasoning.

Abstract. We show how the amalgamation of Logic Programming with probabilistic reasoning enhances its capabilities for intelligent reasoning.

A new language, Probabilistic finite domains, is described and it can be used to code code two examples and the benefits of the probabilistic information ...

We show how the amalgamation of Logic Programming with probabilistic reasoning enhances its capabilities for intelligent reasoning.

... {Extending the {CLP} engine for Reasoning under Uncertainty}, Booktitle = {14th International Symposium on Methodologies for Intelligent Systems}, Month ...

Extending the CLP Engine for Reasoning under Uncertainty · A Survey of First-Order Probabilistic Models · Probabilistic Space Partitioning in Constraint Logic ...

We propose a new way of extending Logic Programming (LP) for reasoning with uncertainty. Probabilistic finite domains (Pfd) capitalise on ideas introduced ...

Together, these results show the versatility of the PITA algorithm, and how the implementation can be easily adapted to support different types of uncertain ...

This work improves decision makers' ability to analyze queues in uncertain environments using a principled method that provably converges to the true parameter ...

Here, we present an expressive language that extends logic clauses with probabilities which are calculated by guards encoding arbitrary relations. We also ...