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...

The current energy transition and the underlying growth in variable uncertain renewable-based generation challenge proper operation of power systems. Classical probabilistic uncertainty models, e.g., stochastic programming or robust optimisation, have been used widely to solve problems such as day-ahead reserve dispatch problem enhance decisions with a insight renewable real-time. By doing so, ...

Lean manufacturing is a strategic concern for companies which conduct mass production and it has become even more significant for those producing in a project-oriented way by modularization.  In this paper, a bi-objective optimization model is proposed to design and plan a supply chain up to the final assembly centre. The delivery time and the quality in the procurement and low fluctuation of t...

This paper studies the emergency service facility location problem in an uncertain environment. The main focus is the integration of uncertainty regarding the covered area due to uncertain traveling times. Previous approaches only consider either probabilistic or fuzzy optimization to cope with uncertainty. However, in many real-world problems the required statistical parameters are not precise...

support vector regression (svr) solves regression problems based on the concept of support vector machine (svm). in this paper, a new model of svr with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...

Verification of PCTL properties of MDPs with convex uncertainties has been investigated recently by Puggelli et al. However, model checking algorithms typically suffer from the state space explosion problem. In this paper, we discuss the use of probabilistic bisimulation to reduce the size of such an MDP while preserving the PCTL properties it satisfies. As a core part, we show that deciding bi...

There has been a substantial recent focus on the concept of probabilistic programming [6] towards its positioning as a prominent paradigm for advancing and facilitating the development of machine-learning applications. A probabilisticprogramming language typically consists of two components: a specification of a stochastic process (the prior), and a specification of observations that restrict t...