A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of and reasoning with complex, structured probability distributions. Examples include functional languages (Church [Goodman et al., 2008], IBAL [Pfeffer, 2001]), object-oriented languages (Figaro [Pfeffer, 2009]), and logic languages (Pr...

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...

We introduce a new approach to probabilistic logic programming in which probabilities are defined over a set of possible worlds. More precisely, classical program clauses are extended by a subinterval of [0; 1℄ that describes a range for the conditional probability of the head of a clause given its body. We then analyze the complexity of selected probabilistic logic programming tasks. It turns ...

Uncertain information is being taken into account in an increasing number of application fields. In the meantime, abduction has been proved a powerful tool for handling hypothetical reasoning and incomplete knowledge. Probabilistic logical models are suitable framework to handle uncertain information, last decade many probabilistic languages have proposed, as well inference learning systems the...