Abstract
Due to the increasing need to individualize mass products, product configurators are becoming more and more a manifest in the environment of business to customer retailers. Furthermore, technology-driven companies try to formalize expert knowledge to maintain their most valuable asset – their technological know-how. Consequently, insulated and diversified knowledge bases are created leading to complex challenges whenever knowledge needs to be consolidated. In this paper, we present the ConMerge-Algorithm which can integrate two constraint-based knowledge bases by applying redundancy detection and conflict detection. Based on detected conflicts, our algorithm applies resolution strategies and assures consistency of the resulting knowledge bases. Furthermore, the user can choose the operation mode of the algorithm: keeping all configuration solutions of each individual input knowledge base or only solutions which are valid in both original knowledge bases. With this method of knowledge base arbitration, the ability to consolidating distributed product configuration knowledge bases is provided.
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Uta, M., Felfernig, A. (2020). ConMerge – Arbitration of Constraint-Based Knowledge Bases. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_12
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