Principle and Application for Rumination Computing Algorithms

Ping Zhu; Pohua Lv; Weiming Zou; Xuetao Jiang; Jin Shi; Yang Zhang; Yirong Ma

doi:doi:10.11648/j.acm.20241305.18

Research Article |

| Peer-Reviewed

Principle and Application for Rumination Computing Algorithms

Ping Zhu^*

, Pohua Lv

, Weiming Zou

, Xuetao Jiang

, Jin Shi

, Yang Zhang

, Yirong Ma

Published in Applied and Computational Mathematics (Volume 13, Issue 5)

Received: 6 September 2024 Accepted: 25 October 2024 Published: 29 October 2024

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Abstract

To fully analyze, mine, and utilize the information and knowledge implied in problem resolving use cases, this paper proposed the autonomous learning method based on machine inducting, hypothesis formulating, and result verifying, which was similar to the biological process of cows ruminating, called rumination computing. Firstly, after inducting and summarizing over 1080 mathematic application problem, the system architecture and general algorithm for humanoid automatic resolving mathematic application problems were represented, which typically included functional modules such as commonsense knowledge base, domain knowledge base, and local knowledge base, preprocessing, word segmentation and part of speech tagging, semantic framework matching, global semantic analyzing, thinking mechanism implementing, etc. Secondly, after the use case solutions were approved, three typical rumination computing modes, including vocabulary sequence, semantic relationship, and computing action, were introduced based on the correct results, resolving steps, and basic rumination actions. The rumination computing step plan was formulated, new knowledge was obtained from the commonsense and results verification, so the continuous autonomous learning loop for machine thinking was formed. Detailed explanations were provided for the three core algorithms implemented (rumination framework algorithm, rumination semantic algorithm, rumination action algorithm). Then, by specific mathematic application problem humanoid resolving user cases, the above three types of rumination computing modes were illumined.

Published in	Applied and Computational Mathematics (Volume 13, Issue 5)
DOI	10.11648/j.acm.20241305.18
Page(s)	193-209
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Rumination Computing, Mathematic Application Problem Resolving, Explainable Artificial Intelligence, Semantic Understanding, Thinking Mechanism

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APA Style

Zhu, P., Lv, P., Zou, W., Jiang, X., Shi, J., et al. (2024). Principle and Application for Rumination Computing Algorithms. Applied and Computational Mathematics, 13(5), 193-209. https://doi.org/10.11648/j.acm.20241305.18

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ACS Style

Zhu, P.; Lv, P.; Zou, W.; Jiang, X.; Shi, J., et al. Principle and Application for Rumination Computing Algorithms. Appl. Comput. Math. 2024, 13(5), 193-209. doi: 10.11648/j.acm.20241305.18

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AMA Style

Zhu P, Lv P, Zou W, Jiang X, Shi J, et al. Principle and Application for Rumination Computing Algorithms. Appl Comput Math. 2024;13(5):193-209. doi: 10.11648/j.acm.20241305.18

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@article{10.11648/j.acm.20241305.18,
  author = {Ping Zhu and Pohua Lv and Weiming Zou and Xuetao Jiang and Jin Shi and Yang Zhang and Yirong Ma},
  title = {Principle and Application for Rumination Computing Algorithms
},
  journal = {Applied and Computational Mathematics},
  volume = {13},
  number = {5},
  pages = {193-209},
  doi = {10.11648/j.acm.20241305.18},
  url = {https://doi.org/10.11648/j.acm.20241305.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20241305.18},
  abstract = {To fully analyze, mine, and utilize the information and knowledge implied in problem resolving use cases, this paper proposed the autonomous learning method based on machine inducting, hypothesis formulating, and result verifying, which was similar to the biological process of cows ruminating, called rumination computing. Firstly, after inducting and summarizing over 1080 mathematic application problem, the system architecture and general algorithm for humanoid automatic resolving mathematic application problems were represented, which typically included functional modules such as commonsense knowledge base, domain knowledge base, and local knowledge base, preprocessing, word segmentation and part of speech tagging, semantic framework matching, global semantic analyzing, thinking mechanism implementing, etc. Secondly, after the use case solutions were approved, three typical rumination computing modes, including vocabulary sequence, semantic relationship, and computing action, were introduced based on the correct results, resolving steps, and basic rumination actions. The rumination computing step plan was formulated, new knowledge was obtained from the commonsense and results verification, so the continuous autonomous learning loop for machine thinking was formed. Detailed explanations were provided for the three core algorithms implemented (rumination framework algorithm, rumination semantic algorithm, rumination action algorithm). Then, by specific mathematic application problem humanoid resolving user cases, the above three types of rumination computing modes were illumined.
},
 year = {2024}
}

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TY  - JOUR
T1  - Principle and Application for Rumination Computing Algorithms

AU  - Ping Zhu
AU  - Pohua Lv
AU  - Weiming Zou
AU  - Xuetao Jiang
AU  - Jin Shi
AU  - Yang Zhang
AU  - Yirong Ma
Y1  - 2024/10/29
PY  - 2024
N1  - https://doi.org/10.11648/j.acm.20241305.18
DO  - 10.11648/j.acm.20241305.18
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 193
EP  - 209
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20241305.18
AB  - To fully analyze, mine, and utilize the information and knowledge implied in problem resolving use cases, this paper proposed the autonomous learning method based on machine inducting, hypothesis formulating, and result verifying, which was similar to the biological process of cows ruminating, called rumination computing. Firstly, after inducting and summarizing over 1080 mathematic application problem, the system architecture and general algorithm for humanoid automatic resolving mathematic application problems were represented, which typically included functional modules such as commonsense knowledge base, domain knowledge base, and local knowledge base, preprocessing, word segmentation and part of speech tagging, semantic framework matching, global semantic analyzing, thinking mechanism implementing, etc. Secondly, after the use case solutions were approved, three typical rumination computing modes, including vocabulary sequence, semantic relationship, and computing action, were introduced based on the correct results, resolving steps, and basic rumination actions. The rumination computing step plan was formulated, new knowledge was obtained from the commonsense and results verification, so the continuous autonomous learning loop for machine thinking was formed. Detailed explanations were provided for the three core algorithms implemented (rumination framework algorithm, rumination semantic algorithm, rumination action algorithm). Then, by specific mathematic application problem humanoid resolving user cases, the above three types of rumination computing modes were illumined.

VL  - 13
IS  - 5
ER  -

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Author Information

Ping Zhu

Beijing Broad Network & Information Company Limited, Beijing, China; Tellhow Institute of Smart City, Beijing, China

Contact Email

http://orcid.org/0000-0002-5608-9070
Pohua Lv

Beijing Broad Network & Information Company Limited, Beijing, China

Contact Email

http://orcid.org/0009-0000-4598-1568
Weiming Zou

Beijing Tellhow Intelligent Engineering Company Limited, Beijing, China

Contact Email

http://orcid.org/0009-0008-1233-2922
Xuetao Jiang

Beijing Broad Network & Information Company Limited, Beijing, China

Contact Email

http://orcid.org/0009-0000-1264-4856
Jin Shi

Beijing Tellhow Intelligent Engineering Company Limited, Beijing, China

Contact Email

http://orcid.org/0009-0002-2768-8143
Yang Zhang

Beijing Yizhuang Smart City Institute Group Company Limited, Beijing, China

Contact Email

http://orcid.org/0009-0004-3594-8835
Yirong Ma

Beijing Tellhow Intelligent Engineering Company Limited, Beijing, China

Contact Email

http://orcid.org/0009-0005-8180-184X

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Plain Text BibTeX RIS

APA Style

Zhu, P., Lv, P., Zou, W., Jiang, X., Shi, J., et al. (2024). Principle and Application for Rumination Computing Algorithms. Applied and Computational Mathematics, 13(5), 193-209. https://doi.org/10.11648/j.acm.20241305.18

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ACS Style

Zhu, P.; Lv, P.; Zou, W.; Jiang, X.; Shi, J., et al. Principle and Application for Rumination Computing Algorithms. Appl. Comput. Math. 2024, 13(5), 193-209. doi: 10.11648/j.acm.20241305.18

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AMA Style

Zhu P, Lv P, Zou W, Jiang X, Shi J, et al. Principle and Application for Rumination Computing Algorithms. Appl Comput Math. 2024;13(5):193-209. doi: 10.11648/j.acm.20241305.18

Copy | Download

@article{10.11648/j.acm.20241305.18,
  author = {Ping Zhu and Pohua Lv and Weiming Zou and Xuetao Jiang and Jin Shi and Yang Zhang and Yirong Ma},
  title = {Principle and Application for Rumination Computing Algorithms
},
  journal = {Applied and Computational Mathematics},
  volume = {13},
  number = {5},
  pages = {193-209},
  doi = {10.11648/j.acm.20241305.18},
  url = {https://doi.org/10.11648/j.acm.20241305.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20241305.18},
  abstract = {To fully analyze, mine, and utilize the information and knowledge implied in problem resolving use cases, this paper proposed the autonomous learning method based on machine inducting, hypothesis formulating, and result verifying, which was similar to the biological process of cows ruminating, called rumination computing. Firstly, after inducting and summarizing over 1080 mathematic application problem, the system architecture and general algorithm for humanoid automatic resolving mathematic application problems were represented, which typically included functional modules such as commonsense knowledge base, domain knowledge base, and local knowledge base, preprocessing, word segmentation and part of speech tagging, semantic framework matching, global semantic analyzing, thinking mechanism implementing, etc. Secondly, after the use case solutions were approved, three typical rumination computing modes, including vocabulary sequence, semantic relationship, and computing action, were introduced based on the correct results, resolving steps, and basic rumination actions. The rumination computing step plan was formulated, new knowledge was obtained from the commonsense and results verification, so the continuous autonomous learning loop for machine thinking was formed. Detailed explanations were provided for the three core algorithms implemented (rumination framework algorithm, rumination semantic algorithm, rumination action algorithm). Then, by specific mathematic application problem humanoid resolving user cases, the above three types of rumination computing modes were illumined.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Principle and Application for Rumination Computing Algorithms

AU  - Ping Zhu
AU  - Pohua Lv
AU  - Weiming Zou
AU  - Xuetao Jiang
AU  - Jin Shi
AU  - Yang Zhang
AU  - Yirong Ma
Y1  - 2024/10/29
PY  - 2024
N1  - https://doi.org/10.11648/j.acm.20241305.18
DO  - 10.11648/j.acm.20241305.18
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 193
EP  - 209
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20241305.18
AB  - To fully analyze, mine, and utilize the information and knowledge implied in problem resolving use cases, this paper proposed the autonomous learning method based on machine inducting, hypothesis formulating, and result verifying, which was similar to the biological process of cows ruminating, called rumination computing. Firstly, after inducting and summarizing over 1080 mathematic application problem, the system architecture and general algorithm for humanoid automatic resolving mathematic application problems were represented, which typically included functional modules such as commonsense knowledge base, domain knowledge base, and local knowledge base, preprocessing, word segmentation and part of speech tagging, semantic framework matching, global semantic analyzing, thinking mechanism implementing, etc. Secondly, after the use case solutions were approved, three typical rumination computing modes, including vocabulary sequence, semantic relationship, and computing action, were introduced based on the correct results, resolving steps, and basic rumination actions. The rumination computing step plan was formulated, new knowledge was obtained from the commonsense and results verification, so the continuous autonomous learning loop for machine thinking was formed. Detailed explanations were provided for the three core algorithms implemented (rumination framework algorithm, rumination semantic algorithm, rumination action algorithm). Then, by specific mathematic application problem humanoid resolving user cases, the above three types of rumination computing modes were illumined.

VL  - 13
IS  - 5
ER  -

Copy | Download