This paper presents KReator, a versatile integrated development environment for probabilistic inductive logic programming currently under development. The area of probabilistic inductive logic programming (or statistical relational learning) aims at applying probabilistic methods of inference and learning in relational or first-order representations of knowledge. In the past ten years the commu...

This thesis describes work on two applications of probabilistic programming: the learning of probabilistic program code given specifications, in particular program code of one-dimensional samplers; and the facilitation of sequential Monte Carlo inference with help of data-driven proposals. The latter is presented with experimental results on a linear Gaussian model and a non-parametric dependen...

ProbLog is a probabilistic programming language based on Prolog. The new ProbLog system called ProbLog2 can solve a range of inference and learning tasks typical for the Probabilistic Graphical Models (PGM) and Statistical Relational Learning (SRL) communities. The main mechanism behind ProbLog2 is a conversion of the given program to a weighted Boolean formula. We argue that this conversion ap...

Stochastic Approach to Vehicle Routing Problem: Development and Theories Abstract In this article, a chance constrained (CCP) formulation of the Vehicle Routing Problem (VRP) is proposed. The reality is that once we convert some special form of probabilistic constraint into their equivalent deterministic form then a nonlinear constraint generates. Knowing that reliable computer software...

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...

In this paper we show the embedding of Hybrid Probabilistic Logic Programs into the rather general framework of Residuated Logic Programs, where the main results of (deenite) logic programming are validly extrapolated. The importance of this result is that for the rst time a framework encompassing several quite distinct logic programming more general semantical structure paving the way for deen...

Programming tools based on probabilistic models of massive codebases (aka “Big Code”) promise to solve important programming tasks that were difficult or practically infeasible to address before. However, building such tools requires solving a number of hard problems at the intersection of programming languages, program analysis and machine learning. In this paper we summarize some of our exper...

We present a probabilistic inductive logic programming framework which integrates non-monotonic reasoning, probabilistic inference and parameter learning. In contrast to traditional approaches to probabilistic Answer Set Programming (ASP), our framework imposes only comparatively little restrictions on probabilistic logic programs in particular, it allows for ASP as well as FOL syntax, and for ...

factors in mind, the Dutch PRM-group has concentrated its research mainly on the basic problems of efficient implementation. First we introduce functional programming languages , discussing advantages, disadvantages and implementation issues. Then we address the important topic of the underlying reduction model. Furthermore we discuss the sequential and parallel implementation of the model we h...

Preface The past several years have witnessed a tremendous interest in logic-based probabilistic learning as testified by the number of formalisms and systems and their applications. Logic-based probabilistic learning is a multidisciplinary research area that integrates relational or logic formalisms, probabilistic reasoning mechanisms, and machine learning and data mining principles. Logic-bas...