Possibilistic constraint satisfaction problems or “how to handle soft constraints?”
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Product Configuration Frameworks-A Survey
IEEE Intelligent Systems
UCP-Networks: A Directed Graphical Representation of Conditional Utilities
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Semiring-Based CSPs and Valued CSPs: Basic Properties and Comparison
Over-Constrained Systems
Efficient utility functions for ceteris paribus preferences
Eighteenth national conference on Artificial intelligence
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Qualitative decision making in adaptive presentation of structured information
ACM Transactions on Information Systems (TOIS)
Compact value-function representations for qualitative preferences
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A Hybrid Framework for Over-Constrained Generalized
Artificial Intelligence Review
A hybrid framework for over-constrained generalized resource-constrained project scheduling problems
Artificial Intelligence Review
Hard and soft constraints for reasoning about qualitative conditional preferences
Journal of Heuristics
Extended constraint handling for CP-networks
Intelligent information processing II
Solving abduction by computing joint explanations
Annals of Mathematics and Artificial Intelligence
Semiring-Based Soft Constraints
Concurrency, Graphs and Models
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
Handling conditional preferences in recommender systems
Proceedings of the 14th international conference on Intelligent user interfaces
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
mCP nets: representing and reasoning with preferences of multiple agents
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
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
Database preferences queries: a possibilistic logic approach with symbolic priorities
FoIKS'08 Proceedings of the 5th international conference on Foundations of information and knowledge systems
Preferences in AI: An overview
Artificial Intelligence
Computational techniques for a simple theory of conditional preferences
Artificial Intelligence
ICLP'05 Proceedings of the 21st international conference on Logic Programming
Database preference queries--a possibilistic logic approach with symbolic priorities
Annals of Mathematics and Artificial Intelligence
Top-k retrieval using conditional preference networks
Proceedings of the 21st ACM international conference on Information and knowledge management
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Many real life optimization problems contain both hard and soft constraints, as well as qualitative conditional preferences. However, there is no single formalism to specify all three kinds of information. We therefore propose a framework, based on both CP-nets and soft constraints, that handles both hard and soft constraints as well as conditional preferences efficiently and uniformly. We study the complexity of testing the consistency of preference statements, and show how soft constraints can faithfully approximate the semantics of conditional preference statements whilst improving the computational complexity.