Optimizing Inference for Probabilistic Logic Programs Exploiting Independence and Exclusiveness

In this work we present an algorithm called Deep Parameter learning for HIerarchical probabilistic Logic pro- grams (DPHIL) that learns hierarchical PLP parameters using

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Cette micrographie nous permet de constater l’état poreux de l’échantillon avec la présence de grains ayant une forme particulière et qui rappellent la rosace ou plutôt une

As stated in the Introduction, the dependencies between random variables implicit in the control and data flow of a PLP, and the impossibility of completely representing the set of

We have introduced T P -compilation, a novel anytime in- ference approach for probabilistic logic programs that com- bines the advantages of forward reasoning with state-of-the-

– We presented an argumentation semantics of defeasible logic programs, based on conflict resolution strategies within assumption-based frameworks, whose semantics satisfies

When termination and non-termination analysis produce complementary results for a logic procedure, then with respect to the leftmost selection rule and the language used to

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