Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Constraint Propagation and Value Acquisition: Why we should do it Interactively
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on 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
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Constraint Processing
Winner determination in sequential majority voting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Semiring-Based Soft Constraints
Concurrency, Graphs and Models
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Solving weighted constraint satisfaction problems with memetic/exact hybrid algorithms
Journal of Artificial Intelligence Research
The Knowledge Engineering Review
Interval-valued soft constraint problems
Annals of Mathematics and Artificial Intelligence
Practical voting rules with partial information
Autonomous Agents and Multi-Agent Systems
On the modelling and optimization of preferences in constraint-based temporal reasoning
Artificial Intelligence
Hi-index | 0.00 |
We consider soft constraint problems where some of the preferences may be unspecified. This models, for example, situations with several agents providing the data, or with possible privacy issues. In this context, we study how to find an optimal solution without having to wait for all the preferences. In particular, we define an algorithm to find a solution which is necessarily optimal, that is, optimal no matter what the missing data will be, with the aim to ask the user to reveal as few preferences as possible. Experimental results show that in many cases a necessarily optimal solution can be found without eliciting too many preferences.