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CP-nets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements

by: Craig Boutilier, Ronen I. Brafman, Carmel Domshlak, Holger H. Hoos, and David Poole

In: J. Artif. Intell. Res. JAIR, Vol. 21 (2004) , p. 135-191.

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URL:	http://www.cs.washington.edu/research/jair/abstracts/boutilier04a.html

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.

Description

DBLP Record 'journals/jair/BoutilierBDHP04'

BibTeX record

@article{boutilierBDHP04,
  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 = {2008-11-14T21:01:27.000+0100},
  author = {Boutilier, Craig and Brafman, Ronen I. and Domshlak, Carmel and Hoos, Holger H. and Poole, David},
  biburl = {http://www.bibsonomy.org/bibtex/2c3066f89335933a4d241816ab7c5f27e/neilernst},
  description = {DBLP Record 'journals/jair/BoutilierBDHP04'},
  interhash = {79562c67dde4ce00b16c8d8042fdf020},
  intrahash = {c3066f89335933a4d241816ab7c5f27e},
  journal = {J. Artif. Intell. Res. (JAIR)},
  keywords = {preference},
  pages = {135-191},
  timestamp = {2008-11-14T21:01:27.000+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
}

Endnote record

%0 Journal Article
%1 boutilierBDHP04
%A Boutilier, Craig
%A Brafman, Ronen I.
%A Domshlak, Carmel
%A Hoos, Holger H.
%A Poole, David
%D 2004
%J J. Artif. Intell. Res. (JAIR)
%K 
%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.