Key Violations often occur in real-life datasets, especially in those integrated from different sources. Enforcing constraints strictly on these datasets is not feasible. In this paper we formalize the notion of soft-key constraints on probabilistic databases, which allow for violation of key constraint by penalizing every violating world by a quantity proportional to the violation. To represen...

A continuous distillation process with random inflow rate is considered. The aim is to find a control (feed rate, heat supply, reflux rate) which is optimal with respect to energy consumption and which is robust at the same time with respect to the stochastic level constraints in the feed tank. The solution approach is based on the formulation of probabilistic constraints. An overall model incl...

Constraint-admissible sets have been widely used in the study of control of systems with hard constraints. This paper proposes a generalization of the maximal constraint admissible set for constrained linear discrete time system to the case where chance or probabilistic constraints are present. Defined in the most obvious way, the maximal probabilistic constraint-admissible set is not invariant...

A probabilistic inference rule is a general rule that provides bounds on a target probability given constraints on a number of input probabilities. Example: from P (AjB) r infer P (:AjB) 2 [1 r; 1℄. Rules of this kind have been studied extensively as a deduction method for propositional probabilistic logics. Many different rules have been proposed, and their validity proved – often with substan...

Development of applicable robustness results for stochastic programs with probabilistic constraints is a demanding task. In this paper we follow the relatively simple ideas of output analysis based on the contamination technique and focus on construction of computable global bounds for the optimal value function. Dependence of the set of feasible solutions on the probability distribution rules ...

This paper presents an application of an optimized implementation of a probabilistic description logic defined by Giugno and Lukasiewicz [9] to the domain of image interpretation. This approach extends a description logic with so-called probabilistic constraints to allow for automated reasoning over formal ontologies in combination with probabilistic knowledge. We analyze the performance of cur...

We look at probabilistic logic programs as a specification language for probabilistic models, and study their interpretation and complexity. Acyclic programs specify Bayesian networks, and, depending on constraints on logical atoms, their inferential complexity reaches complexity classes #P, #NP, and even #EXP. We also investigate (cyclic) stratified probabilistic logic programs, showing that t...

This paper is concerned with some feasibility issues in mathematical programs with equilibrium constraints (MPECs) where additional joint constraints are present that must be satis ed by the state and design variables of the problems. We introduce su cient conditions that guarantee the feasibility of these MPECs. It turns out that these conditions also guarantee the feasibility of the quadratic...

Probabilistic logics combine the expressive power of logic with the ability to reason with uncertainty. Several probabilistic logic languages have been proposed in the past, each of them with their own features. In this paper, we propose a new probabilistic constraint logic programming language, which combines constraint logic programming with probabilistic reasoning. The language supports mode...

Graphical representations for probabilistic relationships have recently received considerable attention in A1. Qualitative probabilistic networks abstract from the usual numeric representations by encoding only qualitative relationships, which are inequality constraints on the joint probability distribution over the variables. Although these constraints are insufficient to determine probabiliti...