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Computer Science > Artificial Intelligence

arXiv:2205.00829 (cs)
[Submitted on 26 Apr 2022]

Title:Function Decomposition Tree with Causality-First Perspective and Systematic Description of Problems in Materials Informatics

Authors:Hiori Kino, Hieu-Chi Dam, Takashi Miyake, Riichiro Mizoguchi
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Abstract:As interdisciplinary science is flourishing because of materials informatics and additional factors; a systematic way is required for expressing knowledge and facilitating communication between scientists in various fields. A function decomposition tree is such a representation, but domain scientists face difficulty in constructing it. Thus, this study cites the general problems encountered by beginners in generating function decomposition trees and proposes a new function decomposition representation method based on a causality-first perspective for resolution of these problems. The causality-first decomposition tree was obtained from a workflow expressed according to the processing sequence. Moreover, we developed a program that performed automatic conversion using the features of the causality-first decomposition trees. The proposed method was applied to materials informatics to demonstrate the systematic representation of expert knowledge and its usefullness.
Comments: 41 page, 13 figures
Subjects: Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci)
Cite as: arXiv:2205.00829 [cs.AI]
  (or arXiv:2205.00829v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2205.00829

Submission history

From: Hiori Kino Dr [view email]
[v1] Tue, 26 Apr 2022 08:41:05 UTC (2,526 KB)
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