We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of predicates. show how existing inference and techniques the underlying ProbLog can be adapted for new language. theoretically experimentally demonstrate DeepProbLog supports (i) both symbolic subsymbolic representations inference, (ii) program induction, (iii) (logic) programmi...

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 ...

This paper considers a probabilistic inventory model with uniform leadtime demand and fuzzy cost components under probabilistic and imprecise constraints. Firstly we solve the model by general fuzzy non-linear programming technique. Then intuitionistic fuzzy optimization technique is applied and finally, regarding the optimization of the objective function a comparative study is presented among...

Methods of simulated annealing and genetic programming over probabilistic program traces are developed firstly. These methods combine expressiveness of Turing-complete probabilistic languages, in which arbitrary generative models can be defined, and search effectiveness of meta-heuristic methods. To use these methods, one should only specify a generative model of objects of interest and a fitne...

We make a formal analogy between random sampling and fresh name generation. show that quasi-Borel spaces, model for probabilistic programming, can soundly interpret the ν-calculus, calculus Moreover, we prove this semantics is fully abstract up to first-order types. This surprising an ‘off-the-shelf’ model, requires novel analysis of probability distributions on function spaces. Our tools are d...

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...