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UEHARA Artificial Intelligence Science Research Laboratory

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Last Update at 16:24 on Mar. 14, 2025

UEHARA
Artificial Intelligence Science
Research Laboratory

Kiyohiko Uehara, Dr. Eng.
(received from Tokyo Institute of Technology, currently known as Institute of Science Tokyo)

α-GEM Family
Invented by Dr. Kiyohiko Uehara
Toward Artificial Intelligence
Based on Self-Explaining Fuzzy Inference

α-GEM family consists of new approaches for fuzzy inference and fuzzy rule learning toward their use in artificial intelligence.

α-GEM Family Tree
Learn More

α-GEM Family Tree [22] :
Relations Between the Methods Invented by Dr. Kiyohiko Uehara

The α-GEM family consists of fuzzy inference methods for non-sparse rule base and sparse rule base [22]. In addition, it includes methods for noise reduction and fuzzy rule learning by which fuzzy rules can be efficiently optimized. α-GEMII (α-level-set and generalized-mean-based inference with the proof of two-sided symmetry of consequences) has played a central role in the growth of the α-GEM family.

Note: As the number of papers on α-GEMII is five, the five publication years of the papers are shown near to α-GEMII in Fig. 1.

α-GEMII [8-12] : Fuzzy Inference Proving Convex Consequences

-GEMII is a fuzzy inference method based on α-level set and generalized mean, which is a member of α-GEM family. It can mathematically prove the convexity of consequences by the effective use of the generalized mean, while the fuzzy constraints of given facts are propagated to those of the deduced consequences. Moreover, it can mathematically prove the symmetricity of consequences under some conditions that are axiomatically derived from the viewpoint of fuzzy inference [8-12]. The operational process of the fuzziness-propagation control is not depicted in Fig. 2 for ease of visibility.

α-FUZZ-ES Learning [26] : Fast Fuzzy-Rule Optimization

α-FUZZI-ES learning is a method for the fast optimization of fuzzy rules, which is a member of α-GEM family. α-FUZZI-ES learning makes it possible to optimize fuzzy rules independently of each other. This property reduces the dimensionality of the search space in finding the optimum fuzzy rules. Thereby, α-FUZZI-ES learning can attain fast convergence in fuzzy rule optimization. Moreover, α-FUZZI-ES learning can be efficiently performed with hardware in parallel to optimize fuzzy rules independently of each other. α-FUZZI-ES learning is especially effective when evaluation functions are not differentiable and derivative-based optimization methods cannot be applied to fuzzy rule learning; α-FUZZI-ES learning is superior in fast convergence, especially with the derivative-free optimization of fuzzy rules [26].

Methods in α-GEM Family

The α-GEM family consists of the methods listed in the following table at present. All of these methods were invented by Dr. Kiyohiko Uehara. The α-GEM family is growing. New methods are going to join the α-GEM family.

No.Invented MethodMeaning of the Method NameYear: 1st ProposalMain References
(1)α-RITHMα-level-set and arithmetic-mean-based inference1995[4]
(2)α-GEMα-level-set and generalized-mean-based inference1997[6]
(3)α-GEMIIα-level-set and generalized-mean-based inference with the proof of two-sided symmetry of consequences2009[8-12]
(4)α-GEMSα-cut and generalized-mean-based inference in synergy with composition 2013[17]
(5)α-GEMSTα-level-set and generalized-mean-based inference in synergy with composition via linguistic-truth-value control2016[19]
(6)α-GEMMIα-level-set and generalized-mean-based inference with multi-level interpolation2011[13]
(7)α-GEMMIETα-level-set and generalized-mean-based inference with multi-level interpolation extended in the number of points for interpolation2013[16]
(8)α-GEMILIEα-level-set and generalized-mean-based inference with infinite-level interpolation2013[15]
(9)α-GEMINASα-level-set and generalized-mean-based inference with fuzzy rule interpolation at an infinite number of activating points2015[18]
(10)α-GEMINASTα-level-set and generalized-mean-based inference in synergy between α-GEMINAS and the composition via linguistic-truth-value control2017[21]
(11)α-GEMI-ESα-GEMINAS-based local-evolution toward slight linearity for global smoothness2017[23]
(12)α-GEMI-SINGα-GEMII and α-GEMINAS-based local-evolution toward slight linearity for global smoothness by using singleton input–output rules2018[24]
(13)α-FUZZI-ESα-weight-based fuzzy-rule independent evaluations2019[26]
(14)α-FUZZI-ES Learningα-FUZZI-ES-based fuzzy-rule learning2019[26]
(15)α-FUZZI-EXα-weight-based fuzzy-rule independent evaluations extended for fuzzy inputs2020[25]
(16)α-FUZZI-EX Learningα-FUZZI-EX-based fuzzy-rule learning2020[25]
(17)α-GEMII-X Denoisingα-GEMII-based denoising to unify fuzzy inference and preprocessing for fuzzy rule optimization2021[27]

Books

[book1]K. Uehara, "Chapter 2: Fundamentals of Fuzzy Control," Practical Fuzzy-Control Engineering, Editorial Supervisor: T. Terano, The Institute of Electronics, Information and Communication Engineers, pp. 15-39, 1991 (in Japanese).
The English translations of the chapter and book titles are for convenience and are not official. The original ones in Japanese are as follows:
上原 清彦,「第２章 ファジィ制御の基礎理論」, 寺野 寿郎 監修『実用ファジィ制御技術』, 電子情報通信学会, pp.15-39, 1991.
[book2]K. Uehara, "Chapter 4: Fuzzy Systems Theory," Introduction to Intelligence Engineering, Editorial Supervisor and Author: K. Hirota, Shokodo, pp. 63-83, 1996 (in Japanese).
The English translations of the chapter and book titles are for convenience and are not official. The original ones in Japanese are as follows:
上原 清彦, 「第４章 ファジィ理論」, 廣田 薫 編著『知能工学概論』, 昭晃堂, pp. 63-83, 1996.
[book3]K. Uehara and K. Hirota, “Chapter 11: Fuzzy Inference with Control Schemes for Fuzzy Constraint Propagation,” Fuzzy Theory Systems: Techniques and Applications, Volume 1, Editorial Supervisor: C. T. Leondes, Academic Press, pp. 267-290, 1999.
[book4]K. Uehara, "Fuzzy Inference Based on Similarity Measures," "Constraint-Propagation-Type Fuzzy Inference," Handbook on Fuzzy Systems and Soft Computing, Editor-in-Chief: M. Mukaidono, Edition of Japan Society for Fuzzy Theory, KYORITSU SHUPPAN Co., LTD., pp. 120-125, 2000 (in Japanese).
The English translations of the chapter and book titles are for convenience and are not official. The original ones in Japanese are as follows:
上原 清彦,「類似性測度に基づいたファジィ推論法」「制約伝播型ファジィ推論法」, 向殿 政男 編集委員長『ファジィとソフトコンピューティング ハンドブック』(日本ファジィ学会編), 共立出版, pp. 120-125, 2000.
[book5]K. Uehara and K. Hirota, “Chapter 4: Fuzzy Connection Admission Control for ATM Networks,” Editors: W. Pedrycz and A. V. Vasilakos, Computational Intelligence in Telecommunications Networks, pp. 87-107, 2000.

Main Papers

[1]K. Uehara, E. Taguchi, T. Watahiki, and T. Miyata, “A Data Converter for Analog/Fuzzy-Logic Interface,” The Trans. of the Institute of Electronics and Communication Engineers, Vol.J67-C, No.4, pp. 391-396, 1984 (in English and in Japanese).
[2]K. Uehara and M. Fujise, “Fuzzy Inference Based on Families of α-Level Sets,” IEEE Transactions on Fuzzy Systems, Vol. 1, No. 2, pp. 111–124, 1993.
[3]K. Uehara and M. Fujise, “Multistage Fuzzy Inference Formulated as Linguistic-Truth-Value Propagation and its Learning Algorithm Based on Back-Propagating Error Information,” IEEE Transactions on Fuzzy Systems, Vol. 1, No. 3, pp. 205–221, 1993.
[4]K. Uehara, “Fuzzy Inference Based on a Weighted Average of Fuzzy Sets and its Learning Algorithm for Fuzzy Exemplars,” Proc. of the International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and the Second International Fuzzy Engineering Symposium (FUZZ-IEEE/IFES’95), Vol. IV, pp. 2253–2260, 1995.
[5]K. Uehara and K. Hirota, “Fuzzy Connection Admission Control for ATM Networks Based on Possibility Distribution of Cell Loss Ratio,” IEEE Journal on Selected Areas in Communications, Vol. 15, No. 2, pp. 179–190, 1997.
[6]K. Uehara and K. Hirota, “Parallel Fuzzy Inference Based on α-Level Sets and Generalized Means,” Information Sciences (International Journal), Vol. 100, No. 1–4, pp. 165–206, 1997.
[7]K. Uehara and K. Hirota, “Parallel and Multistage Fuzzy Inference Based on Families of α-Level Sets,” Information Sciences (International Journal ), Vol. 106, No. 1-2, pp. 159–195, 1998.
[8]K. Uehara, T. Koyama, and K. Hirota, “Fuzzy Inference with Schemes for Guaranteeing Convexity and Symmetricity in Consequences Based on α-Cuts,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 13, No. 2, pp. 135–149, 2009.
[9]K. Uehara, T. Koyama, and K. Hirota, “Inference with Governing Schemes for Propagation of Fuzzy Convex Constraints Based on α-Cuts,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 13, No. 3, pp. 321–330, 2009.
[10]K. Uehara, T. Koyama, and K. Hirota, “Inference Based on α-Cut and Generalized Mean with Fuzzy Tautological Rules,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 14, No. 1, pp. 76–88, 2010.
[11]K. Uehara, T. Koyama, and K. Hirota, “Suppression Effect of α-Cut Based Inference on Consequence Deviations,&dquo; Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 14, No. 3, pp. 256–271, 2010.
[12]K. Uehara, T. Koyama, and K. Hirota, “Inference Based on α-Cut and Generalized Mean in Representing Fuzzy-Valued Functions,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 14, No. 6, pp. 581–592. 2010.
[13]K. Uehara, S. Sato, and K. Hirota, “Inference for Nonlinear Mapping with Sparse Fuzzy Rules Based on Multi-Level Interpolation,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 15, No. 3, pp. 264–287, 2011.
[14]K. Tsukamoto and K. Uehara, “A Map Matching Method with Fuzzy Inference” IEEJ Transactions on Electronics, Information and Systems, Vol. 132, No. 2, 334-335, 2012 (in Japanese).
[15]K. Uehara and K. Hirota, “Infinite-Level Interpolation for Inference with Sparse Fuzzy Rules: Fundamental Analysis Toward Practical Use,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 17, No. 1, pp. 44–59, 2013.
[16]K. Uehara and K. Hirota, “Multi-Level Interpolation for Inference with Sparse Fuzzy Rules: An Extended Way of Generating Multi-Level Points,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 17, No. 2, pp. 127–148, 2013.
[17]K. Uehara and K. Hirota, “Multi-Level Control of Fuzzy-Constraint Propagation in Inference Based on α-Cuts and Generalized Mean,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 17, No. 4, pp. 647–662, 2013.
[18]K. Uehara and K. Hirota, “Inference with Fuzzy Rule Interpolation at an Infinite Number of Activating Points,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 19, No. 1, pp. 74–90, 2015.
[19]K. Uehara and K. Hirota, “Multi-Level Control of Fuzzy Constraint Propagation via Evaluations with Linguistic Truth Values in Generalized-Mean-Based Inference,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 20, No. 2, pp. 355–377, 2016.
[20]K. Uehara and K. Hirota, “Fuzzy Inference: Its Past and Prospects,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 21, No. 1, pp. 13–19, 2017.
[21]K. Uehara and K. Hirota, “Multi-Level Control of Fuzzy-Constraint Propagation in Inference with Fuzzy Rule Interpolation at an Infinite Number of Activating Points,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 21, No. 3, pp. 425–447, 2017.
[22]K. Uehara and K. Hirota, “Fuzzy Inference Based on α-Cuts and Generalized Mean: Relations Between the Methods in its Family and their Unified Platform,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 21, No. 4, pp. 597–615, 2017.
[23]K. Uehara and K. Hirota, “Noise Reduction with Inference Based on Fuzzy Rule Interpolation at an Infinite Number of Activating Points: Toward fuzzy rule learning in a unified inference platform,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 22, No. 6, pp. 883–899, 2018.
[24]K. Uehara and K. Hirota, “Noise Reduction with Fuzzy Inference Based on Generalized Mean and Singleton Input–Output Rules: Toward fuzzy rule learning in a unified inference platform,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 23, No. 6, pp. 1027–1043, 2019.
[25]K. Uehara and K. Hirota,“Independent Evaluations of Each Fuzzy Rule for Derivative-Free Optimization of Fuzzy Systems: Toward Fast Fuzzy-Rules Learning for Fuzzy Inputs,” Proceedings of the 9th International Symposiumon Computational Intelligence and IndustrialApplications (ISCIIA2020), 1A2-2-4, pp. 1–8, 2020.
[26]K. Uehara and K. Hirota, “A Fast Method for Fuzzy Rules Learning with Derivative-Free Optimization by Formulating Independent Evaluations of Each Fuzzy Rule,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 25, No. 2, pp. 213–225, 2021.
[27]K. Uehara,“Noise Reduction by Fuzzy Inference Based on α-Cuts and Generalized Mean: A Feasibility Study,” Proceedings of the 7th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2021), M2-6-5, pp. 1–7, 2021.
[28]K. Uehara,“Continual Learning with Fuzzy Inference Based on Derivative-Free Independent Optimization of Each Fuzzy Rule: A Feasibility Study,” Proceedings of Continual Learning and Emergence of Intelligent Systems, Japan Society for Fuzzy Theory and Intelligent Informatics, p. 23, Dec. 2023.

Awards

No.AwardJournal/Conference Name
(1)JACIII Best Paper Award 2011Journal of Advanced Computational Intelligence and Intelligent Informatics
(2)ISCIIA2016 Best Paper AwardThe 7th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2016), 2016.
(3)ISCIIA2016 Session Best Presentation AwardThe 7th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2016), 2016.
(4)IWACIII2017 Session Best Presentation AwardThe 5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2017), 2017.
(5)ISCIIA-ITCA2018 Session Best Presentation AwardThe 8th International Symposiumon Computational Intelligence and IndustrialApplications (ISCIIA2018), The 12th China-Japan International Workshop on Information Technology and Control Applications (ITCA2018), 2018.
(6)IWACIII2019 Session Best Presentation AwardThe 6th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2019), 2019.
(7)JACIII Outstanding Reviewer Award 2019Journal of Advanced Computational Intelligence and Intelligent Informatics
(8)ISCIIA2020 Session Best Presentation AwardThe 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020), 2020.

Main Recent Activities

YearOfficial PositionJournal/Conference Name
2024-presentAdvisory EditorJournal of Advanced Computational Intelligence and Intelligent Informatics
2021-presentCouncilor*
（評議員）		Japan Society for Fuzzy Theory and Intelligent Informatics
（日本知能情報ファジィ学会）
2019-2021Director*
（理事, 広報委員長）		Japan Society for Fuzzy Theory and Intelligent Informatics
（日本知能情報ファジィ学会）
2004-2024Editorial MemberJournal of Advanced Computational Intelligence and Intelligent Informatics
2021ChairpersonThe 7th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2021)
2021Program Committee MemberThe 7th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2021)
2017-2020Vice Editor-in-Chief for Papers*
（論文副委員長）		Editorial Board, Japan Society for Fuzzy Theory and Intelligent Informatics
（日本知能情報ファジィ学会 会誌編集委員会）
2020ChairpersonThe 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)
2020Program Committee MemberThe 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)
2020Program Committee MemberJoint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems (SCIS&ISIS2020)
2019Program Committee MemberThe 6th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2019)
2018Program Committee MemberThe 8th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2018), The 12th China-Japan International Workshop on Information Technology and Control Applications (ITCA2018).
2017Program Committee MemberThe 5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2017)
2016Program Committee MemberThe 7th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2016)
2017Associate EditorJournal of Advanced Computational Intelligence and Intelligent Informatics
2013Guest EditorJournal of Advanced Computational Intelligence and Intelligent Informatics
* The Japanese title is translated into English for convenience and the English translation is not official.

Profile

Kiyohiko Uehara, Dr. Eng. [Received from Tokyo Institute of Technology, currently known as Institute of Science Tokyo]

Research Interests

Fuzzy Systems Theory, Fuzzy Inference, Fuzzy Logic, Fuzzy Rule Learning, Artificial Intelligence Based on Fuzzy Inference, Artificial Immune Systems, Deep Learning, Explainable Artificial Intelligence (XAI).

Affiliation

UEHARA Artificial Intelligence Science Research Laboratory, Representative, Research Scientist

Work Experience

  • Toshiba Corporation, Research and Development Center, Electronics Equipment Laboratory.
  • ATR Optical and Radio Communications Research Laboratories, Researcher.
    ATR: Advanced Telecommunications Research Institute International, Kyoto, Japan. The history of ATR is shown here.
  • Toshiba Corporation, Kansai Research Laboratory, Research Scientist.
  • Toshiba Corporation, Research and Development Center, Communication and Information Systems Research Laboratory, Research Scientist.
  • Toshiba Corporation, Research and Development Center, Communication Platform Laboratory, Research Scientist.
  • Ibaraki University, Graduate School of Science and Engineering, Asociate Professor.
  • UEHARA Artificial Intelligence Science Research Laboratory, Representative, Research Scientist, at present.

Membership in Academic Societies

  • IEEE (The Institute of Electrical and Electronics Engineers)
  • Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
  • The Japanese Society for Artificial Intelligence (JSAI)
  • The Institute of Electronics, Information and Communication Engineers (IEICE)

Profile in Japanese

上原 清彦, 博士(工学) [東京工業大学 (現 東京科学大学) 授与]

研究分野

ファジィ理論, ファジィ推論, ファジィ論理, ファジィルール学習, ファジィ推論に基づいた人工知能, 人工免疫システム, ディープラーニング(深層学習), 説明可能な人工知能 (XAI: Explainable Artificial Intelligence).

所属

UEHARA Artificial Intelligence Science Research Laboratory, 代表, リサーチサイエンティスト.

職務経験

  • 東京芝浦電気株式会社 [現 株式会社 東芝],  総合研究所 [現 研究開発センター], 電子機器研究所.
  • ATR光電波通信研究所, 研究員.
    ATR: Advanced Telecommunications Research Institute International, 京都. ATRの歴史については このリンク先を参照.
  • 株式会社 東芝, 関西研究所, 研究主務.
  • 株式会社 東芝,  研究開発センター, 情報・通信システム研究所, 研究主務.
  • 株式会社 東芝,  研究開発センター, 通信プラットホームラボラトリー, 研究主務.
  • 茨城大学大学院 理工学研究科 [博士前期課および博士後期課程], 准教授.
  • UEHARA Artificial Intelligence Science Research Laboratory, 代表, リサーチサイエンティスト (現職).

所属学会

  • IEEE (The Institute of Electrical and Electronics Engineers)
  • 日本知能情報ファジィ学会
  • 人工知能学会
  • 電子情報通信学会

News

  • Jun. 3, 2024: This homepage has been renewed.
  • Apr. 1, 2024: UEHARA Artificial Intelligence Science Research Laboratory has been established by Dr. Kiyohiko Uehara.

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