Information about user preferences plays a key role in automated decision making. In
many domains it is desirable to assess such preferences in a qualitative rather than quantitative
way. In this paper, we propose a qualitative graphical representation of preferences
that reflects conditional dependence and independence of preference statements under a
ceteris paribus (all else being equal) interpretation. Such a representation is often compact
and arguably quite natural in many circumstances. We provide a formal semantics for this
model, and describe how the structure of the network can be exploited in several inference
tasks, such as determining whether one outcome dominates (is preferred to) another, ordering
a set outcomes according to the preference relation, and constructing the best outcome
subject to available evidence.
%0 Journal Article
%1 CP-nets
%A Boutilier, Craig
%A Brafman, Ronen
%A Domshlak, Carmel
%A Hoos, Holger
%A Poole, David
%D 2004
%J Journal of Artificial Intelligence Research
%K cp nets
%P 135--191
%T CP-nets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements
%U http://www.cs.washington.edu/research/jair/abstracts/boutilier04a.html
%V 21
%X Information about user preferences plays a key role in automated decision making. In
many domains it is desirable to assess such preferences in a qualitative rather than quantitative
way. In this paper, we propose a qualitative graphical representation of preferences
that reflects conditional dependence and independence of preference statements under a
ceteris paribus (all else being equal) interpretation. Such a representation is often compact
and arguably quite natural in many circumstances. We provide a formal semantics for this
model, and describe how the structure of the network can be exploited in several inference
tasks, such as determining whether one outcome dominates (is preferred to) another, ordering
a set outcomes according to the preference relation, and constructing the best outcome
subject to available evidence.
@article{CP-nets,
abstract = {Information about user preferences plays a key role in automated decision making. In
many domains it is desirable to assess such preferences in a qualitative rather than quantitative
way. In this paper, we propose a qualitative graphical representation of preferences
that reflects conditional dependence and independence of preference statements under a
ceteris paribus (all else being equal) interpretation. Such a representation is often compact
and arguably quite natural in many circumstances. We provide a formal semantics for this
model, and describe how the structure of the network can be exploited in several inference
tasks, such as determining whether one outcome dominates (is preferred to) another, ordering
a set outcomes according to the preference relation, and constructing the best outcome
subject to available evidence.},
added-at = {2016-11-24T22:51:15.000+0100},
author = {Boutilier, Craig and Brafman, Ronen and Domshlak, Carmel and Hoos, Holger and Poole, David},
biburl = {https://www.bibsonomy.org/bibtex/2ac2f28038b1286503cb0ee68715814bc/nosebrain},
interhash = {79562c67dde4ce00b16c8d8042fdf020},
intrahash = {ac2f28038b1286503cb0ee68715814bc},
journal = {Journal of Artificial Intelligence Research},
keywords = {cp nets},
pages = {135--191},
timestamp = {2016-11-24T22:51:15.000+0100},
title = {{CP-nets}: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements},
url = {http://www.cs.washington.edu/research/jair/abstracts/boutilier04a.html},
volume = 21,
year = 2004
}