AbstractSoft constraints are gaining popularity in diverse areas such as orchestration of Web services or optimization of scheduling decisions. However, current approaches to soft constraints preclude them from modelling certain decision problems with multiple preference criteria. We propose a new approach to soft constraints which allows a natural expression of these problems, describe an implementation in the rewriting logic system Maude, and prove its correctness
Soft constraints extend classical constraints to represent multiple consistency levels, and thus pro...
Soft constraints extend classical constraints to represent multiple consistency levels, and thus pro...
Constraints and quantitative preferences, or costs, are very useful for modelling many real-life pr...
AbstractSoft constraints are gaining popularity in diverse areas such as orchestration of Web servic...
AbstractSoft constraints extend classical constraints to deal with non-functional requirements, over...
Constraints and quantitative preferences, or costs, are very useful for modelling many real-life pr...
Soft constraints are a generalization of classical constraints, which allow for the description of p...
Soft constraint formalisms are an abstract representation of Constraint Satisfaction Problems (CSPs)...
Most problems can be expressed in terms of requirements that must be met by their expected solutions...
Constraint satisfaction problems have generally been recognized as a useful framework for the repres...
In traditional constraint satisfaction, constraints are ``hard\u27\u27 in the sense that we need to ...
We define interval-valued soft constraints, where users can associate an interval of preference valu...
are “hard ” in the sense that we need to satisfy them all. In many practical situations, however, co...
Representing and reasoning with an agent's preferences is important in many applications of constrai...
Constraints are useful to model many real-life problems. Soft constraints are even more useful, sinc...
Soft constraints extend classical constraints to represent multiple consistency levels, and thus pro...
Soft constraints extend classical constraints to represent multiple consistency levels, and thus pro...
Constraints and quantitative preferences, or costs, are very useful for modelling many real-life pr...
AbstractSoft constraints are gaining popularity in diverse areas such as orchestration of Web servic...
AbstractSoft constraints extend classical constraints to deal with non-functional requirements, over...
Constraints and quantitative preferences, or costs, are very useful for modelling many real-life pr...
Soft constraints are a generalization of classical constraints, which allow for the description of p...
Soft constraint formalisms are an abstract representation of Constraint Satisfaction Problems (CSPs)...
Most problems can be expressed in terms of requirements that must be met by their expected solutions...
Constraint satisfaction problems have generally been recognized as a useful framework for the repres...
In traditional constraint satisfaction, constraints are ``hard\u27\u27 in the sense that we need to ...
We define interval-valued soft constraints, where users can associate an interval of preference valu...
are “hard ” in the sense that we need to satisfy them all. In many practical situations, however, co...
Representing and reasoning with an agent's preferences is important in many applications of constrai...
Constraints are useful to model many real-life problems. Soft constraints are even more useful, sinc...
Soft constraints extend classical constraints to represent multiple consistency levels, and thus pro...
Soft constraints extend classical constraints to represent multiple consistency levels, and thus pro...
Constraints and quantitative preferences, or costs, are very useful for modelling many real-life pr...