Prospects of Continual Causality for Industrial Applications

Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu
Proceedings of The First AAAI Bridge Program on Continual Causality, PMLR 208:18-24, 2023.

Abstract

We have been investigating the causal analysis of industrial plant process data and its various applications, such as material quantity optimization utilizing intervention effects. However, process data often comes with various problems such as non-stationary characteristics including distribution shifts, which make such applications difficult. When combined with the idea of continual learning, causal models may be able to solve these problems. We present the potential and prospects for industrial applications of continual causality, showing previous work. We also briefly introduce our causal discovery method utilizing a continual framework.

Cite this Paper

BibTeX
@InProceedings{pmlr-v208-fujiwara23a, title = {Prospects of Continual Causality for Industrial Applications}, author = {Fujiwara, Daigo and Koyama, Kazuki and Kiritoshi, Keisuke and Okawachi, Tomomi and Izumitani, Tomonori and Shimizu, Shohei}, booktitle = {Proceedings of The First AAAI Bridge Program on Continual Causality}, pages = {18--24}, year = {2023}, editor = {Mundt, Martin and Cooper, Keiland W. and Dhami, Devendra Singh and Ribeiro, Adéle and Smith, James Seale and Bellot, Alexis and Hayes, Tyler}, volume = {208}, series = {Proceedings of Machine Learning Research}, month = {07--08 Feb}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v208/fujiwara23a/fujiwara23a.pdf}, url = {https://proceedings.mlr.press/v208/fujiwara23a.html}, abstract = {We have been investigating the causal analysis of industrial plant process data and its various applications, such as material quantity optimization utilizing intervention effects. However, process data often comes with various problems such as non-stationary characteristics including distribution shifts, which make such applications difficult. When combined with the idea of continual learning, causal models may be able to solve these problems. We present the potential and prospects for industrial applications of continual causality, showing previous work. We also briefly introduce our causal discovery method utilizing a continual framework.} }
Endnote
%0 Conference Paper %T Prospects of Continual Causality for Industrial Applications %A Daigo Fujiwara %A Kazuki Koyama %A Keisuke Kiritoshi %A Tomomi Okawachi %A Tomonori Izumitani %A Shohei Shimizu %B Proceedings of The First AAAI Bridge Program on Continual Causality %C Proceedings of Machine Learning Research %D 2023 %E Martin Mundt %E Keiland W. Cooper %E Devendra Singh Dhami %E Adéle Ribeiro %E James Seale Smith %E Alexis Bellot %E Tyler Hayes %F pmlr-v208-fujiwara23a %I PMLR %P 18--24 %U https://proceedings.mlr.press/v208/fujiwara23a.html %V 208 %X We have been investigating the causal analysis of industrial plant process data and its various applications, such as material quantity optimization utilizing intervention effects. However, process data often comes with various problems such as non-stationary characteristics including distribution shifts, which make such applications difficult. When combined with the idea of continual learning, causal models may be able to solve these problems. We present the potential and prospects for industrial applications of continual causality, showing previous work. We also briefly introduce our causal discovery method utilizing a continual framework.
APA
Fujiwara, D., Koyama, K., Kiritoshi, K., Okawachi, T., Izumitani, T. & Shimizu, S.. (2023). Prospects of Continual Causality for Industrial Applications. Proceedings of The First AAAI Bridge Program on Continual Causality, in Proceedings of Machine Learning Research 208:18-24 Available from https://proceedings.mlr.press/v208/fujiwara23a.html.

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