Uncertainty in Artificial Intelligence
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More-or-Less CP-Networks
Fusun Yaman, Marie desJardins
Preferences play an important role in our everyday lives. CP-networks, or CP-nets in short, are graphical models for representing conditional qualitative preferences under ceteris paribus ("all else being equal") assumptions. Despite their intuitive nature and rich representation, dominance testing with CP-nets is computationally complex, even when the CP-nets are restricted to binary-valued preferences. Tractable algorithms exist for binary CP-nets, but these algorithms are incomplete for multi-valued CPnets. In this paper, we identify a class of multivalued CP-nets, which we call more-or-less CPnets, that have the same computational complexity as binary CP-nets. More-or-less CP-nets exploit the monotonicity of the attribute values and use intervals to aggregate values that induce similar preferences. We then present a search control rule for dominance testing that effectively prunes the search space while preserving completeness.
Pages: 434-441
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PDF Link: /papers/07/p434-yaman.pdf
AUTHOR = "Fusun Yaman and Marie desJardins",
TITLE = "More-or-Less CP-Networks",
BOOKTITLE = "Proceedings of the Twenty-Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-07)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2007",
PAGES = "434--441"

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