Probabilistic Logic Programming is an effective formalism for encoding problems characterized by uncertainty. Some of these may require the optimization probability values subject to constraints among distributions random variables. Here, we introduce a new class probabilistic logic programs, namely Optimizable Programs, and provide algorithm find best assignment probabilities variables, such t...

Abstract A ProbLog program is a logic with facts that only hold specified probability. In this contribution, we extend language by the ability to answer “What if” queries. Intuitively, defines distribution solving system of equations in terms mutually independent predefined Boolean random variables. theory causality, Judea Pearl proposes counterfactual reasoning for such systems equations. Base...

We introduce PHFL, a probabilistic extension of higher-order fixpoint logic, which can also be regarded as temporal logics such PCTL and the $\mu^p$-calculus. show that PHFL is strictly more expressive than $\mu^p$-calculus, model-checking problem for finite Markov chains undecidable even $\mu$-only, order-1 fragment PHFL. Furthermore full far expressive: we give translation from Lubarsky's $\m...

We introduce probabilistic many-valued logic programs in which the implication connective is interpreted as material implication. We show that probabilistic many-valued logic programming is computationally more complex than classical logic programming. More precisely, some deduction problems that are P-complete for classical logic programs are shown to be co-NP-complete for probabilistic many-v...

1 In [20], a new Hybrid Probabilistic Logic Programs framework is proposed, and a new semantics is developed to enable encoding and reasoning about real-world applications. In this paper, we extend the language of Hybrid Probabilistic Logic Programs framework in [20] to allow non-monotonic negation, and define two alternative semantics: stable probabilistic model semantics and probabilistic wel...

In [23], a new Hybrid Probabilistic Logic Programs framework has been proposed, and a new semantics has been developed to enable encoding and reasoning about real-world applications. In this paper, the language of Hybrid Probabilistic Logic Programs framework of [23] is extended to allow non-monotonic negation, and two alternative semantics are defined: stable probabilistic model semantics and ...

Probabilistic logics have attracted a great deal of attention during the past few years. Where logical languages have, already from the inception of the field of artificial intelligence, taken a central position in research on knowledge representation and automated reasoning, probabilistic graphical models with their associated probabilistic basis have taken up in recent years a similar positio...