This paper proposes a number of models for integrating finitedomain stochastic constraint solvers into constraint logic programming systems to solve constraint-satisfaction problems efficiently. Stochastic solvers can solve hard constraint-satisfaction problems very efficiently, and constraint logic programming allows heuristics and problem breakdown to be encoded in the same language as the co...

Cost-based filtering is a novel approach that combines techniques from Operations Research and Constraint Programming to filter from decision variable domains values that do not lead to better solutions [7]. Stochastic Constraint Programming is a framework for modeling combinatorial optimization problems that involve uncertainty [19]. In this work, we show how to perform cost-based filtering fo...

This paper proposes a general framework for integrating a constraint logic programming system with a stochastic constraint solver to solve constraint satisfaction problems eeciently. Stochas-tic solvers can solve hard constraint satisfaction problems very eeciently, and constraint logic programming allows heuristics and problem breakdown to be encoded in the same language as the constraints. He...

We present a stochastic version of Concurrent Constraint Programming (CCP), where we associate a rate to each basic instruction that interacts with the constraint store. We give an operational semantic that can be provided either with a discrete or a continuous model of time. The notion of observables is discussed, both for the discrete and the continuous version, and a connection between the t...

Combinatorial optimisation problems often contain uncertainty that has to be taken into account to produce realistic solutions. One way of describing the uncertainty is using scenarios, where each scenario describes different potential sets of problem parameters based on random distributions or historical data. While efficient algorithmic techniques exist for specific problem classes such as li...

This panel explores issues of systematic and stochastic control in the context of constraint satisfaction.

Constraint automata have been used as an operational model for component connectors that coordinate the cooperation and communication of the components by means of a network of channels. In this paper, we introduce a variant of constraint automata (called continuous-time constraint automata) that allows us to specify time-dependent stochastic assumptions about the channel connections or the com...