Preferences in Constraint Satisfact

Preferences in Constraint Satisfaction and Optimization
We review constraint-based ap- proaches to handle preferences. We start by defining the main notions of constraint programming and then give various concepts of soft constraints and show how they can be used to model quantitative preferences. We then consider how soft constraints can be adapted to handle other forms of preferences, such as bipolar, qualitative, and temporal preferences. Finally, we describe how AI techniques such as abstraction, explanation generation, machine learning, and preference elicitation can be useful in modeling and solving soft constraints.

Результаты (английский) 1:

Preferences in Constraint Satisfaction and Optimization We review the constraint-based ap-proaches to handle preferences. We start by defining the main notions of constraint programming and then give various concepts of soft constraints and show how they can be used to model the quantitative preferences. We then consider how soft constraints can be adapted to handle other forms of preferences, such as bipolar, you, and temporal preferences. Finally, we describe how AI techniques such as abstraction, explanation, generation, machine learning, and preference elicitation can be useful in modeling and solving soft constraints.

Результаты (английский) 2:

Constraint Satisfaction in Preferences and Optimization
We review constraint-based ap- proaches to handle preferences. We start by defining the main notions of constraint programming and then give various concepts of soft constraints and show how they can be used to model quantitative preferences. We then consider how soft constraints can be adapted to handle other forms of preferences, such as bipolar, qualitative, and temporal preferences. Finally, we describe how AI techniques such as abstraction, explanation generation, machine learning, and preference elicitation can be useful in modeling and solving soft constraints.