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From soft constraints to bipolar preferences: modelling framework and solving issues
Journal article   Peer reviewed

From soft constraints to bipolar preferences: modelling framework and solving issues

Stefano Bistarelli, Maria Silvia Pini, Francesca Rossi and K. Brent Venable
Journal of experimental & theoretical artificial intelligence, Vol.22(2), pp.135-158
06/2010
DOI: https://doi.org/10.1080/09528130903010212
Web of Science ID: WOS:000277018500003

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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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