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...

Probabilistic inductive logic programming, sometimes also called statistical relational learning, addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with first order logic representations and machine learning. A rich variety of different formalisms and learning techniques have been developed. In the present paper, we start from inductiv...

In this paper, we integrate, implement, and validate formation flying algorithms for large number of agents using probabilistic swarm guidance with inhomogeneous Markov chains and model predictive control with sequential convex programming. Using an inhomogeneous Markov chain, each agent determines its target position during each time step in a statistically independent manner while the swarm c...

Heinrich’s (1931) classical study implies that most industrial accidents can be characterized as a probabilistic result of human error. The present research quantifies Heinrich’s observation and compares four descriptive models of decision making in the abstracted setting. The suggested quantification utilizes signal detection theory (Green & Swets, 1966). It shows that Heinrich’s observation c...

The Critical Path Method (CPM) and its development to probabilistic environment, the Program Evaluation and Review Technique (PERT), are the most common tools for predicting and managing different short time or long time projects. However, one of the main difficulties in using mathematical models in real world applications is the vagueness and uncertainty of data and parameters such as activity...

Probabilistic logic programming under the distribution semantics has been very useful in machine learning. However, inference is expensive so machine learning algorithms may turn out to be slow. In this paper we consider a restriction of the language called hierarchical PLP in which clauses and predicates are hierarchically organized. In this case the language becomes truth-functional and infer...

Anglican is a probabilistic programming system designed to interoperate with Clojure and other JVM languages. We describe the implementation of Anglican and illustrate how its design facilitates both explorative and industrial use of probabilistic programming.

We present ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog. The ProbLog language allows the user to intuitively build programs that do not only encode complex interactions between a large sets of heterogenous components but also the inherent uncertainties that are present in real-life situations. The system provides efficient algorithms for queryi...

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 for lar...

The term probabilistic constrained programming means the same as chance constrained programming, i.e., optimization of a function subject to certain conditions where at least one is formulated so that a condition, involving random variables, should hold with a prescribed probability. The probability is usually not prescribed exactly but a lower bound is given instead which is in practice near u...