Reasoning with conditional ceteris paribus preference statements
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Compact value-function representations for qualitative preferences
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Graphically structured value-function compilation
Artificial Intelligence
Relaxing Ceteris Paribus Preferences with Partially Ordered Priorities
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
ICSOC '08 Proceedings of the 6th International Conference on Service-Oriented Computing
An Efficient Upper Approximation for Conditional Preference
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Mastering the Processing of Preferences by Using Symbolic Priorities in Possibilistic Logic
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
An Efficient Deduction Mechanism for Expressive Comparative Preferences Languages
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A quantitative model for user preferences based on qualitative specifications
Proceedings of the 2009 international conference on Pervasive services
Extending CP-nets with stronger conditional preference statements
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Constraint-based preferential optimization
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
On graphical modeling of preference and importance
Journal of Artificial Intelligence Research
The computational complexity of dominance and consistency in CP-Nets
Journal of Artificial Intelligence Research
The computational complexity of dominance and consistency in CP-nets
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Efficient inference for expressive comparative preference languages
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Discovering relative importance of skyline attributes
Proceedings of the VLDB Endowment
Preference-based adaptation of multimedia presentations for different display sizes
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Session continuity and splitting of multimedia applications using qualitative user preferences
Mobility '09 Proceedings of the 6th International Conference on Mobile Technology, Application & Systems
Agreeing on social outcomes using individual CP-nets
Multiagent and Grid Systems - Planning in multiagent systems
Learning conditional preference networks
Artificial Intelligence
An Efficient Procedure for Collective Decision-making with CP-nets
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Efficient skyline evaluation over partially ordered domains
Proceedings of the VLDB Endowment
Preference elicitation in prioritized skyline queries
The VLDB Journal — The International Journal on Very Large Data Bases
Preferences in AI: An overview
Artificial Intelligence
Computational techniques for a simple theory of conditional preferences
Artificial Intelligence
Conditional preferences in software stakeholders' judgments
Proceedings of the 2011 ACM Symposium on Applied Computing
Conditional lexicographic orders in constraint satisfaction problems
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Expert Systems with Applications: An International Journal
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The ability to make decisions and to assess potential courses of action is a comer-stone of many AI applications, and usually this requires explicit information about the decision-maker's preferences. In many applications, preference elicitation is a serious bottleneck. The user either does not have the time, the knowledge, or the expert support required to specify complex multi-attribute utility functions. In such cases, a method that is based on intuitive, yet expressive, preference statements is required. In this paper we suggest the use of TCP-nets, an enhancement of CP-nets, as a tool for representing, and reasoning about qualitative preference statements. We present and motivate this framework, define its semantics, and show how it can be used to perform constrained optimization.