Generative programming: methods, tools, and applications
Generative programming: methods, tools, and applications
Alloy: a lightweight object modelling notation
ACM Transactions on Software Engineering and Methodology (TOSEM)
Product Configuration Frameworks-A Survey
IEEE Intelligent Systems
Smodels - An Implementation of the Stable Model and Well-Founded Semantics for Normal LP
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
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
Software engineering and formal methods
Communications of the ACM - Enterprise information integration: and other tools for merging data
Finding Minimal Unsatisfiable Cores of Declarative Specifications
FM '08 Proceedings of the 15th international symposium on Formal Methods
SAT-based answer set programming
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Reasoning about Conditional Constraint Specifications
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
Clasp: a conflict-driven answer set solver
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
Dynamic constraint satisfaction problems
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Conditional existence of variables in generalized constraint networks
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Automated reasoning on feature models
CAiSE'05 Proceedings of the 17th international conference on Advanced Information Systems Engineering
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Product configuration is a major industrial application domain for constraint satisfaction techniques. Conditional constraint satisfaction problems (CCSPs) and feature models (FMs) have been developed to represent configuration problems in a natural way. CCSPs are like constraint satisfaction problems (CSPs), but they also include potential variables, which might or might not exist in any given solution, as well as classical variables, which are required to take a value in every solution. CCSPs model, for example, options on a car, for which the style of sunroof (a variable) only makes sense if the car has a sunroof at all. FMs are directed acyclic graphs of features with constraints on edges. FMs model, for example, cell phone features, where utility functions are required, but the particular utility function ???games??? is optional, but requires Java support. We show that existing techniques from formal methods and answer set programming can be used to naturally model CCSPs and FMs. We demonstrate configurators in both approaches. An advantage of these approaches is that the model builder does not have to reformulate the CCSP or FM into a classic CSP, converting potential variables into classical variables by adding a ???does not exist??? value and modifying the problem constraints. Our configurators automatically reason about the model itself, enumerating all solutions and discovering several kinds of model flaws.