Knowledge-based configuration is one of the most successfully applied technologies of Artificial Intelligence. Configuration systems (configurators) support users in the composition of pre-defined parts to an integrated product (or service). Example application domains for configuration technologies are telecommunication, financial services, and the automotive industry.

A major challenge for the successful commercial application of configuration technologies are time-intensive and error-prone development and maintenance processes related to configuration knowledge bases. The major goal of the ICONE project (Intelligent Assistence for Configuration Knowledge Base Development and Maintenance) was to significantly simplify development and maintenance processes for configuration knowledge bases. In this context, the following research results have been developed:

1. Recommender technologies for the pro-active support of knowledge engineers and domain experts in the development and maintenance of configuration knowledge bases (e.g., recommendation of constraints that should be adapted).
2. Intelligent analysis and test methods for the efficient identification of errors in knowledge bases (e.g., on the basis of algorithms that support the determination of preferred diagnoses).
3. Complexity metrics and refactoring rules for the identification of early counter measures in the case of “unintended developments” in the configuration knowledge base.