R1 (“XCON”) at age 12: lessons from an elementary school achiever
Artificial intelligence in perspective
Midwinters, end games, and body parts: a classification of part-whole relations
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
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
Generating Tradeoffs for Interactive Constraint-Based Configuration
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Partial Constraint Satisfaction
Over-Constrained Systems
A Generative Constraint Formalism for Configuration Problems
AI*IA '93 Proceedings of the Third Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Decomposing and Distributing Configuration Problems
AIMSA '02 Proceedings of the 10th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Constraint Processing
The description logic handbook
Decomposition strategies for configuration problems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Formal methods for the validation of automotive product configuration data
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Towards a general ontology of configuration
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Decomposition of planning problems
AI Communications
An overview of knowledge‐based configuration
AI Communications
A declarative framework for work process configuration
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
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This article deals with the configuration task. Configuring means assembling a set of predefined components in order to build a composite object that meets a set of requirements. Here we present 𝒞𝒪𝒞𝒪𝒩ℱ, an approach to configuration based on a conceptual encoding of the configuration knowledge, directly exploitable by a software configuration system to compute configurations. In particular, we show how Conflict-Directed Backjumping can be adapted to the proposed framework and in which way the efficiency of the configurator may be still enhanced by two look-ahead mechanisms, which exploit the characteristics of the modeling language and the explicit representation of both the compositional structure and the taxonomic relations among component types. The configuration algorithms are explained in detail; the assumptions and the properties which they are based on are explicitly stated. Formal proofs are provided for the basic properties. A set of experimental results on three different real-world domains are presented, which prove the suitability of the approach.