1.1 Abstract We view constraints as relations and constraint satisfaction as a dynamic process of approaching the solution set of the constraints. We have developed a semantic model for dynamic systems, called Constraint Nets, to provide a real-time programming semantics and to model and analyze dynamic systems. In this paper, we explore the relationship between constraint satisfaction and cons...

In this paper we develop convex relaxations of chance constrained optimization problems in order to obtain lower bounds on the optimal value. Unlike existing statistical lower bounding techniques, our approach is designed to provide deterministic lower bounds. We show that a version of the proposed scheme leads to a tractable convex relaxation when the chance constraint function is affine with ...

In this paper, the recognition of temporal scenarios is expressed by the mean of a proximity between a scenario which represents a possible system’s behavior and a session which describes the observed behaviour. This recognition is qualified with a proximity index which allows to classify on-line or out-line the scenarios candidates for an evolution’s explanation. This approach, when used in a ...

Despite the importance of pointing-device movement to efficiency in interfaces, little is known on how target shape impacts speed, acceleration, and other kinematic properties of motion. In this paper, we examine which kinematic characteristics of motion are impacted by amplitude and directional target constraints in Fitts-style pointing tasks. Our results show that instantaneous speed, acceler...

In this work we introduce a novel approach, based on sampling, for finding policies that are likely to be solutions to stochastic constraint satisfaction problems and constraint optimisation problems. Our approach reduces the size of the original problem being analysed and it guarantees that, with a given confidence probability, the policies produced by solving this reduced problem satisfy the ...

The wide applicability of chance–constrained programming, together with advances in convex optimization and probability theory, has created a surge of interest in finding efficient methods for processing chance constraints in recent years. One of the successes is the development of so–called safe tractable approximations of chance–constrained programs, where a chance constraint is replaced by a...

In this paper we study ambiguous chance constrained problems where the distributions of the random parameters in the problem are themselves uncertain. We primarily focus on the special case where the uncertainty set Q of the distributions is of the form Q = {Q : ρp(Q, Q0) ≤ β}, where ρp denotes the Prohorov metric. The ambiguous chance constrained problem is approximated by a robust sampled pro...