E-GENET shows certain success on extending GENET for non-binary CSP's. However, the generic constraint representation scheme of E-GENET induces the problem of storing too many penalty values in constraint nodes and the min-connicts heuristic is not eecient enough on some problems. To overcome these two weaknesses and further improve the performance, we propose several modiications. All of them ...

Bounds are given on the number of steps sufficient for convergence of simulation algorithms on domains of nonnegative integer constraint sets.

Many real world discrete optimization problems are expressible as nested problems where we solve one optimization or satisfaction problem as a subproblem of a larger meta problem. Nested problems include many important problem classes such as: stochastic constraint satisfaction/optimization, quantified constraint satisfaction/optimization and minimax problems. In this paper we define a new clas...

The contribution deals with a stochastic process which cumulates random increments at random moments, the cumulative process. A multiplicative model of rate of cumulation, with regression on covariate processes, is studied. We show the consistency of estimators and then the asymptotic normality of the process of residuals. An application dealing with the process of growing damage of a technical...

To investigate the contributions of taggers or chunkers to the performance of a deep syntactic parser, Weighted Constraint Dependency Grammars have been extended to also take into consideration information from external sources. Using a weak information fusion scheme based on constraint optimization techniques, a parsing accuracy has been achieved which is comparable to other (stochastic) parsers.

Complex multi-stage decision making problems often involve uncertainty, for example, regarding demand or processing times. Stochastic constraint programming was proposed as a way to formulate and solve such decision problems, involving arbitrary constraints over both decision and random variables. What stochastic constraint programming currently lacks is support for the use of factorized probab...

The OT error-driven learner is known to admit guarantees of efficiency, stochastic tolerance and noise robustness which hold independently of any substantive assumptions on the constraints. This paper shows that the HG learner instead does not admit such constraint-independent guarantees. The HG theory of error-driven learning thus needs to be substantially restricted to specific constraint sets.

We consider an infinite-horizon linear-quadratic minimax optimal control problem for stochastic uncertain systems with output measurement. A new description of stochastic uncertainty is introduced using a relative entropy constraint. For the stochastic uncertain system under consideration, a connection between the worst-case control design problem and a specially parametrized risk-sensitive sto...

This article proposes a stochastic vehicle routing problem within the frame-wok of chance constrained programming where one or more parameters are presumed to be random variables with known distribution function. 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 ...

This paper describes a complete and efficient solution to the stochastic allocation and scheduling for Multi-Processor System-on-Chip (MPSoC). Given a conditional task graph characterizing a target application and a target architecture with alternative memory and computation resources, we compute an allocation and schedule minimizing the expected value of communication cost, being the communica...