Contexts—a partitioning concept for hypertext
ACM Transactions on Information Systems (TOIS)
Expert systems for configuration at Digital: XCON and beyond
Communications of the ACM
Contexts: a formalization and some applications
Contexts: a formalization and some applications
The Unified Modeling Language reference manual
The Unified Modeling Language reference manual
A Configuration Tool to Increase Product Competitiveness
IEEE Intelligent Systems
Configuring Large Systems Using Generative Constraint Satisfaction
IEEE Intelligent Systems
Sales Configuration in Business Processes
IEEE Intelligent Systems
COOPIS '98 Proceedings of the 3rd IFCIS International Conference on Cooperative Information Systems
Conceptual modelling for configuration: A description logic-based approach
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Towards a general ontology of configuration
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
SyDeR—System design for reusability
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
An overview of knowledge‐based configuration
AI Communications
Towards a generic model of configuraton tasks
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
Shorter product cycles, lower prices of products, and the production of goods that are tailored to the customers needs made knowledge based product configuration systems a great success of AI technology. However, configuration knowledge bases tend to become large and complex. Therefore, knowledge acquisition and maintenance are crucial phases in the life-cycle of a configuration system. We will show how to meet this challenge by extending a standard design language from the area of Software Engineering with classical description concepts for expressing configuration knowledge. We automatically translate this graphical depiction into logical sentences which can be exploited by a general inference engine to solve the configuration task. In order to cope with usability restrictions of diagrammatic notations for large applications, we introduce the usage of contextual diagrams. This mechanism makes the conceptual model more readable and understandable and supports intuitively the acquisition of functional configuration knowledge.