Add to ORKGTo model combinatorial decision problems involving uncertainty and probability, we extend the stochastic constraint programming framework proposed in [Walsh, 2002] along a number of important dimensions (e.g. to multiple chance constraints and to a range of new objectives). We also provide a new (but equivalent) semantics based on scenarios
Abstract. Constraint Programming (CP) is a very general programming paradigm that proved its efficie...
summary:We study bounding approximations for a multistage stochastic program with expected value con...
We present an extensive study of methods for exactly solving stochastic constraint (optimisation) pr...
To model combinatorial decision problems involving uncertainty and probability, we extend the stocha...
To model combinatorial decision problems involving uncertainty and probability, we extend the stocha...
To model combinatorial decision problems involving uncertainty and probability, we extend the stocha...
To model combinatorial decision problems involving uncertainty and probability, we introduce scenari...
To model decision problems involving uncertainty and probability, we propose stochastic constraint p...
Stochastic programming is concerned with decision making under uncertainty, seeking an optimal polic...
Constraint Programming (CP) is a programming paradigm where relations between variables can be state...
Stochastic programming is concerned with decision making under uncertainty, seeking an optimal polic...
Combinatorial optimisation problems often contain uncertainty that has to be taken into account to p...
Stochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems u...
AbstractStochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for pr...
Complex multi-stage decision making problems often involve uncertainty, for example, regarding deman...
Abstract. Constraint Programming (CP) is a very general programming paradigm that proved its efficie...
summary:We study bounding approximations for a multistage stochastic program with expected value con...
We present an extensive study of methods for exactly solving stochastic constraint (optimisation) pr...
To model combinatorial decision problems involving uncertainty and probability, we extend the stocha...
To model combinatorial decision problems involving uncertainty and probability, we extend the stocha...
To model combinatorial decision problems involving uncertainty and probability, we extend the stocha...
To model combinatorial decision problems involving uncertainty and probability, we introduce scenari...
To model decision problems involving uncertainty and probability, we propose stochastic constraint p...
Stochastic programming is concerned with decision making under uncertainty, seeking an optimal polic...
Constraint Programming (CP) is a programming paradigm where relations between variables can be state...
Stochastic programming is concerned with decision making under uncertainty, seeking an optimal polic...
Combinatorial optimisation problems often contain uncertainty that has to be taken into account to p...
Stochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems u...
AbstractStochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for pr...
Complex multi-stage decision making problems often involve uncertainty, for example, regarding deman...
Abstract. Constraint Programming (CP) is a very general programming paradigm that proved its efficie...
summary:We study bounding approximations for a multistage stochastic program with expected value con...
We present an extensive study of methods for exactly solving stochastic constraint (optimisation) pr...