A sufficient condition for backtrack-bounded search
Journal of the ACM (JACM)
Arc-consistency for non-binary dynamic CSPs
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Consistency restoriation and explanations in dynamic CSPs----application to configuration
Artificial Intelligence
Product Configuration Frameworks-A Survey
IEEE Intelligent Systems
Configuring Large Systems Using Generative Constraint Satisfaction
IEEE Intelligent Systems
Arc-Consistency in Dynamic CSPs Is No More Prohibitive
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Generative constraint-based configuration of large technical systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A classification and constraint-based framework for configuration
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Towards a generic model of configuraton tasks
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Dynamic constraint satisfaction problems
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Eliminating interchangeable values in constraint satisfaction problems
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Neighborhood inverse consistency preprocessing
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Constraint programming techniques are widely used to model and solve interactive decision problems, and especially configuration problems. In this type of application, the configurable product is described by means of a set of constraints bearing on the configuration variables. The user interactively solves the CSP by assigning the variables according to her preferences. The system then has to keep the domains of the other variables consistent with these choices. Since maintaining the global inverse consistency of the domains is not tractable, the domains are instead filtered according to some level of local consistency, e.g. arc-consistency. The present paper aims at offering a more convenient interaction by providing the user with possible alternative values for the already assigned variables, i.e. values that could replace the current ones without leading to a constraint violation. We thus present the new concept of alternative domains in a (possibly) partially assigned CSP. We propose a propagation algorithm that computes all the alternative domains in a single step. Its worst case complexity is comparable to the one of the naive algorithm that would run a full propagation for each variable, but its experimental efficiency is better.