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Journal article
From soft constraints to bipolar preferences: modelling framework and solving issues
Journal of experimental & theoretical artificial intelligence, Vol.22(2), pp.135-158
06/2010
Web of Science ID: WOS:000277018500003
Abstract
Real-life problems present several kinds of preferences. We focus on problems with both positive and negative preferences, which we call bipolar preference problems. Although seemingly specular notions, these two kinds of preferences should be dealt with differently to obtain the desired natural behaviour. We technically address this by generalising the soft constraint formalism, which is able to model problems with one kind of preference. We show that soft constraints model only negative preferences, and we add to them a new mathematical structure which allows to handle positive preferences as well. We also address the issue of the compensation between positive and negative preferences, studying the properties of this operation. Finally, we extend the notion of arc consistency to bipolar problems, and we show how branch and bound (with or without constraint propagation) can be easily adapted to solve such problems.
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Details
- Title
- From soft constraints to bipolar preferences: modelling framework and solving issues
- Publication Details
- Journal of experimental & theoretical artificial intelligence, Vol.22(2), pp.135-158
- Resource Type
- Journal article
- Publisher
- Taylor & Francis Group
- Number of pages
- 24
- Copyright
- © Taylor & Francis, 2009
- Identifiers
- WOS:000277018500003; 99380590597306600
- Academic Unit
- Hal Marcus College of Science and Engineering ; Intelligent Systems and Robotics; Institute for Human and Machine Cognition
- Language
- English