Description Logic Programs (DLP) is an expressive but tractable subset of OWL. In this paper, we study a rising but under-researched problem of learning DLP from uncertain data. Current research rarely explores the plentiful uncertain data populating the Semantic Web. We handle uncertain data in Inductive Logic Programming (ILP) framework by modifying the performance evaluation criteria. We ado...

Project scheduling problem is to make a schedule for allocating the loans to a project such that the total cost and the completion time of the project are balanced under some constraints. This paper presents an uncertain project scheduling problem, of which both the duration times and the resources allocation times are uncertain variables. An uncertain programming model with multiple objectives...

Information Source Tracking (IST) method has been developed recently for the modeling and manipulation of uncertain and inaccurate data in relational databases. In this paper we extend the IST method to deductive databases. We show that positive uncertain databases, i.e. IST-based deductive databases with only positive literals in the heads and the bodies of the rules, enjoy a least model/least...

In standard stochastic dynamic programming, the transition probability distributions of the underlying Markov Chains are assumed to be known with certainty. We focus on the case where the transition probabilities or other input data are uncertain. Robust dynamic programming addresses this problem by defining a min-max game between nature and the controller. Considering examples from inventory a...

In this paper, we examine the duality gap between the robust counterpart of a primal uncertain convex optimization problem and the optimistic counterpart of its uncertain Lagrangian dual and identify the classes of uncertain problems which do not have a duality gap. The absence of a duality gap (or equivalently zero duality gap) means that the primal worst value equals the dual best value. We f...

Based on the chance theory, which is founded for modeling complex systems with not only uncertainty but also randomness, this paper is devoted to studying a new kind of multiobjective programming problem where randomness and uncertainty exist simultaneously, called uncertain random multiobjective programming (URMOP) problem. Since an uncertain random variable does not admit an order relationshi...

This paper addresses the short-term scheduling of chemical process with uncertainty considerations. A multiobjective robust optimization method is proposed to identify Pareto optimal solutions, where Normal boundary intersection (NBI) technique is utilized in order to trace the Pareto optimal surface in the objective space, on which each point represents a trade-off between the various objectiv...

This paper develops the deterministic approach to duality for semi-definite linear programming problems in the face of data uncertainty. We establish strong duality between the robust counterpart of an uncertain semi-definite linear programming model problem and the optimistic counterpart of its uncertain dual. We prove that strong duality between the deterministic counterparts holds under a ch...

......Computing has entered the era of uncertain data, in which hardware and software generate and reason about estimates. New hardware sensors, such as those found in smartphones, fitness devices, cars, homes, and games, observe the physical world around them. Approximate computing deliberately exploits software robustness and unreliable hardware in the name of efficiency. Analog and neuromorp...