Abstract
This paper presents an idea of a creative expert system. It is based on inference and machine learning integration. Execution of learning algorithm is automatic because it is formalized as applying a complex inference rule. Firing such a rule generates intrinsically new knowledge: rules are learned from training data, which consists of facts stored already in the knowledge base. This new knowledge may be used in the same inference chain to derive a decision. Complex rules may also represent other procedural activities, like searching databases. Such a solution makes the reasoning process more creative and allows to continue reasoning in cases when the knowledge base does not have appropriate knowledge explicit encoded. In the paper appropriate model and inference algorithm are proposed. The idea is tested on a decision support system in a casting domain.
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Notes
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Acknowledgments
The research reported in the paper was supported by the grant of The National Centre for Research and Development (LIDER/028/593/L-4/12/NCBR /2013) and by the Polish Ministry of Science and Higher Education under AGH University of Science and Technology Grant 11.11.230.124.
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Sniezynski, B., Legien, G., Wilk-Kołodziejczyk, D., Kluska-Nawarecka, S., Nawarecki, E., Jaśkowiec, K. (2016). Creative Expert System: Result of Inference and Machine Learning Integration. In: Hartmann, S., Ma, H. (eds) Database and Expert Systems Applications. DEXA 2016. Lecture Notes in Computer Science(), vol 9827. Springer, Cham. https://doi.org/10.1007/978-3-319-44403-1_16
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