We present a probabilistic logic programming framework that allows the representation of conditional probabilities. While conditional probabilities are the most commonly used method for representing uncertainty in probabilistic expert systems, they have been largely neglected by work in quantitative logic programming. We de-ne a xpoint theory, declarative semantics, and proof procedure for the ...

Inference is the key bottleneck in probabilistic programming. Often, the main advantages of probabilistic programming – simplicity, modularity, ease-of-use, etc. – are dwarfed by the complexity and intractability of inference. In fact, one of the main reasons for the scarcity/absence of large applications and real-world systems that are based in large part on probabilistic programming languages...

Hybrid probabilistic programs framework [5] is a variation of probabilistic annotated logic programming approach, which allows the user to explicitly encode the available knowledge about the dependency among the events in the program. In this paper, we extend the language of hybrid probabilistic programs by allowing disjunctive composition functions to be associated with heads of clauses and ch...

We survey three applications of mathematical programming to rea soning under uncertainty a an application of linear programming to probabilistic logic b an application of nonlinear programming to Bayesian logic a combination of Bayesian inference with probabilistic logic and c an application of integer programming to Dempster Shafer theory which is a method of combining evidence from di erent s...

Probabilistic models are used in many elds to tackle di erent problems, ranging from image recognition to diagnosing diseases. The advantage of using models is that we can split the encoding of our problem into a probabilistic model from the ways we solve it. We can also classify models to develop some class-speci c, but not problem-speci c algorithms to solve given tasks. These algorithms are ...

Pearl [2000, p. 26] attributes to Laplace [1814] the idea of a probabilistic model as a deterministic system with stochastic inputs. Pearl defines causal models in terms of deterministic systems with stochastic inputs. In this paper, I show how deterministic systems with (independent) probabilistic inputs can also be seen as the basis of modern probabilistic programming languages. Probabilistic...

In 3] a probabilistic space PCSP of processes is constructed uniformly from the standard CSP failures-divergences model. Here we identify the deterministic (probabilistic) elements of PCSP as the maximal elements in the reenement ordering. We extend the standard characterisation of deterministic processes in terms of of properties of their nite approximations, and formulate some analogues of th...

This technical report describes the usage, syntax, semantics and core algorithms of the probabilistic inductive logic programming framework PrASP. PrASP is a research software which integrates non-monotonic reasoning based on Answer Set Programming (ASP), probabilistic inference and parameter learning. In contrast to traditional approaches to Probabilistic (Inductive) Logic Programming, our fra...

Probabilistic programing is an emerging field at the intersection of statistical learning and programming languages. An appealing property of probabilistic programming languages (PPL) is their support for constructing arbitrary probability distributions. This allows one to model many different domains and solve a variety of problems. We show the link between probabilistic planning and PPLs by i...