Abstract
This paper presents comparison of time cost of three proof searching strategies in a creative expert system. Initially, model of the creative expert system and inference algorithm are proposed. The algorithm searches for a proof up to a given maximal depth, using one of the following strategies: finding all possible proofs, finding the first proof by depth-first and finding the first proof by breadth-first. Calculation time is measured in inference scenarios from a casting domain. Creativity of the expert system is achieved thanks to integration of inference and machine learning. The learning algorithm can be automatically executed during inference process, because its execution is formalized as a complex inference rule. Such a rule can be fired during inference process. During execution, training data is prepared from facts already stored in the knowledge base and new implications are learned from it. These implications can be used in the inference process. Therefore, it is possible to infer decisions in cases not covered by the knowledge base explicitly.
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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. (2017). Creative Expert System: Comparison of Proof Searching Strategies. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_38
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