Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Possibilistic constraint satisfaction problems or “how to handle soft constraints?”
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Communications of the ACM
A constraint-based model for cooperative response generation in information dialogues
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The Wasabi Personal Shopper: a case-based recommender system
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Uncertainty in Constraint Satisfaction Problems: a Probalistic Approach
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Learning and Solving Soft Temporal Constraints: An Experimental Study
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Constraint Processing
Learning Preferences on Temporal Constraints: A Preliminary Report
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Temporal constraint reasoning with preferences
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Timid acquisition of constraint satisfaction problems
Proceedings of the 2005 ACM symposium on Applied computing
Partially defined constraints in constraint-based design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
AI Communications - Constraint Programming for Planning and Scheduling
Semiring-Based Soft Constraints
Concurrency, Graphs and Models
Query-driven constraint acquisition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Distance constraints in constraint satisfaction
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A constraint seeker: finding and ranking global constraints from examples
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
ICLP'05 Proceedings of the 21st international conference on Logic Programming
A SAT-based version space algorithm for acquiring constraint satisfaction problems
ECML'05 Proceedings of the 16th European conference on Machine Learning
Two contributions of constraint programming to machine learning
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Constraints are useful to model many real-life problems. Soft constraints are even more useful, since they allow for the use of preferences, which are very convenient in many real-life problems. In fact, most problems cannot be precisely defined by using hard constraints only.However, soft constraint solvers usually can only take as input preferences over constraints, or variables, or tuples of domain values. On the other hand, it is sometimes easier for a user to state preferences over entire solutions of the problem.In this paper, we define an interactive framework where it is possible to state preferences both over constraints and over solutions, and we propose a way to build a system with such features by pairing a soft constraint solver and a learning module, which learns preferences over constraints from preferences over solutions. We also describe a working system which fits our framework, and uses a fuzzy constraint solver and a suitable learning module to search a catalog for the best products that match the user's requirements.