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Journal article
Preferences in Constraint Satisfaction and Optimization
The AI magazine, Vol.29(4), pp.58-68
12/2008
Web of Science ID: WOS:000265659200006
Abstract
We review constraint-based approaches 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 adopted 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.
Details
- Title
- Preferences in Constraint Satisfaction and Optimization
- Publication Details
- The AI magazine, Vol.29(4), pp.58-68
- Resource Type
- Journal article
- Publisher
- John Wiley & Sons Ltd on behalf of Association for the Advancement of Artificial Intelligence
- Number of pages
- 11
- Grant note
- MIUR (Italian Ministry for University and Research); Ministry of Education, Universities and Research (MIUR) Australian Research Council Department of the Broadband, Communications, and Digital Economy
- Copyright
- © 2008 The Authors.
- Identifiers
- WOS:000265659200006; 99380590496406600
- Academic Unit
- Hal Marcus College of Science and Engineering ; Intelligent Systems and Robotics; Institute for Human and Machine Cognition
- Language
- English