As process-orientation continues to be broadly adopted – evidenced by the increasing number of large business process repositories, managing changes in such complex repositories becomes a growing issue. A critical aspect in evolving business processes is change propagation: given a set of primary changes made to a process in a repository, what additional changes are needed to maintain consisten...

In this paper we focus on the relation between models of biological systems consisting of ordinary differential equations (ODE) and models written in a stochastic and concurrent paradigm (sCCP stochastic Concurrent Constraint Programming). In particular, we define a method to associate a set of ODE’s to an sCCP program and a method converting ODE’s into sCCP programs. Then we study the properti...

We explore the relation between the stochastic semantic associated to stochastic Concurrent Constrain Programming (sCCP) and its fluid-flow approximation. Writing the master equation for a sCCP model, we can show that the fluid flow equation is a first-order approximation of the true equation for the average. Moreover, we introduce a second-order correction and first-order equations for the var...

When solving constraint satisfaction problems (CSPs) with stochastic search algorithms (SSAs) using the standard penalty function, it is not possible to show that there is no solution for a problem instance. In this paper we present a hybrid function that can be generally used in conjunction with SSAs to prove unsolvability without changing the search algorithms drastically. We use eight state-...

We discuss a novel approach for dealing with single-stage stochastic constraint satisfaction problems (SCSPs) that include random variables over a continuous or large discrete support. Our approach is based on two novel tools: sampled SCSPs and (α, θ)-solutions. Instead of explicitly enumerating a very large or infinite set of future scenarios, we employ statistical estimation to determine if a...

Two-Stage Problems with Stochastic Order Constraints. Darinka Dentcheva, Department of Mathematical Science, Stevens Institute of Technology. Abstract: We consider two-stage risk-averse stochastic optimization problems with a stochastic-order constraint on the second stage decisions. The usual stochastic order, the increasing convex order and a multivariate stochastic order will be discussed. W...

This paper proposes a new spectral estimation technique based on rational covariance extension with degree constraint. The technique finds a rational spectral density function that approximates given spectral density data under constraint on a covariance sequence. Spectral density approximation problems are formulated as nonconvex optimization problems with respect to a Schur polynomial. To for...

This paper considers a new class of discrete time stochastic uncertain systems in which the uncertainty is described by a constraint on the relative entropy between a nominal noise distribution and the perturbed noise distribution. This uncertainty description is a natural extension to the case of stochastic uncertain systems, of the sum quadratic constraint uncertainty description. The paper s...

We tackle the problem of relating models of systems (mainly biological systems) based on stochastic process algebras (SPA) with models based on differential equations. We define a syntactic procedure that translates programs written in stochastic Concurrent Constraint Programming (sCCP) into a set of Ordinary Differential Equations (ODE), and also the inverse procedure translating ODE’s into sC...

In this paper, we discuss the adaptability of Coevolutionary Genetic Algorithms on dynamic environments. Our CGA consists of two populations: solution-level one and schema-level one. The solution-level population searches for the good solution in a given problem. The schema-level population searches for the good schemata in the former population. Our CGA performs effectively by exchanging genet...