Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
A comparison of structural CSP decomposition methods
Artificial Intelligence
Product Configuration Frameworks-A Survey
IEEE Intelligent Systems
Configuring Large Systems Using Generative Constraint Satisfaction
IEEE Intelligent Systems
An Industrial Strength Description Logics-Based Configurator Platform
IEEE Intelligent Systems
Supporting Product Configuration in a Virtual Store
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
𝒞𝒪𝒞𝒪𝒩ℱ: Conceptual language-based configuration
AI Communications
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In the present work the issue of decomposing and distributing a configuration problem is approached in a framework where the domain knowledge is represented in a structured way by using a KL-One like language, where whole-part relations play a major role in defining the structure of the configurable objects. The representation formalism provides also a constraint language for expressing complex relations among components and subcomponents.The paper presents a notion of boundness among constraints which specifies when two components can be independently configured. Boundness is the basis for partitioning constraints and such a partitioning induces a decomposition of the configuration problem into independent subproblems that are distributed to a pool of configurators to be solved in parallel.Preliminary experimental results in the domain of PC configuration showing the effectiveness of the decomposition technique in a sequential approach to configuration are also presented.