Abstract
This research is concerned with causal understanding and qualitative reasoning of behavior of physical systems, which are crucial issues of modelbased problem solving. In this paper, a new method of qualitative reasoning and causal ordering is proposed and its application to a power plant is presented. The method is based on our kernel ontologies of causality and time-resolution and a domain ontology of fluid systems. These ontologies help make the design rationales of our method explicit and facilitate reusability of our models. The whole of the target system is represented by combining a set of local component models and global constraints. The component models include local and causal characteristics of each component which are independent of context for their reuse on the basis of the ontology of causality. Global constraints with time-scales are derived according to the general properties of the physical entity which are prepared beforehand as a part of the domain ontology. They contribute to providing intuitive causal ordering of complex behavior originated in various configurations of components, including inter-component negative feedback. Furthermore, the method has been successfully applied to a power plant. All the reasoning results matched those obtained by a domain expert including their ambiguities.
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Kitamura, Y., Ikeda, M., Mizoguchi, R. (1996). A qualitative reasoning based on an ontology of fluid systems and its evaluation. In: Foo, N., Goebel, R. (eds) PRICAI'96: Topics in Artificial Intelligence. PRICAI 1996. Lecture Notes in Computer Science, vol 1114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61532-6_25
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DOI: https://doi.org/10.1007/3-540-61532-6_25
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