Similarity Intelligence: Similarity Based Reasoning, Computing, and Analytics

Authors

  • Zhaohao Sun

    Department of Business Studies, PNG University of Technology, Private Mail Bag, Lae 411, Morobe, Papua New Guinea

DOI:

https://doi.org/10.30564/jcsr.v5i3.5575
Received: 19 March 2023 | Revised: 20 May 2023 | Accepted: 26 May 2023 | Published Online: 9 June 2023

Abstract

Similarity has been playing an important role in computer science, artificial intelligence (AI) and data science. However, similarity intelligence has been ignored in these disciplines. Similarity intelligence is a process of discovering intelligence through similarity. This article will explore similarity intelligence, similarity-based reasoning, similarity computing and analytics. More specifically, this article looks at the similarity as an intelligence and its impact on a few areas in the real world. It explores similarity intelligence accompanying experience-based intelligence, knowledge-based intelligence, and data-based intelligence to play an important role in computer science, AI, and data science. This article explores similarity-based reasoning (SBR) and proposes three similarity-based inference rules. It then examines similarity computing and analytics, and a multiagent SBR system. The main contributions of this article are: 1) Similarity intelligence is discovered from experience-based intelligence consisting of data-based intelligence and knowledge-based intelligence. 2) Similarity-based reasoning, computing and analytics can be used to create similarity intelligence. The proposed approach will facilitate research and development of similarity intelligence, similarity computing and analytics, machine learning and case-based reasoning.

Keywords:

Similarity intelligence; Similarity computing; Similarity analytics; Similarity-based reasoning; Big data analytics; Artificial intelligence; Intelligent agents

References

[1] Zimmermann, H.J., 2011. Fuzzy set theory—and its applications. Springer Science & Business Media: Berlin.

[2] Zadeh, L.A., 1971. Similarity relations and fuzzy orderings. Information Sciences. 3(2), 177-200.

[3] Minsky, M., 1988. Society of mind. Simon and Schuster: New York.

[4] Aroraa, C., Chitra, L., Munish, J., 2022. Data analytics: Principles, tools, and practices. BPB Publications: New Dalhi.

[5] Sun, Z., 2022. A mathematical theory of big data. Journal of Computer Science Research. 4(2), 13-23.

[6] Zhang, D.G., Ni, C.H., Zhang, J., et al., 2022. A novel edge computing architecture based on adaptive stratified sampling. Computer Communications. 183, 121-135.

[7] Milošević, P., Petrović, B., Jeremić, V., 2017. IFS-IBA similarity measure in machine learning algorithms. Expert Systems with Applications. 89, 296-305.

[8] Finnie, G., Sun, Z., 2004. Intelligent techniques in E-commerce: A case based reasoning perspective. Springer-Verlag: Berlin.

[9] Sun, R., 1995. Robust reasoning: Integrating rule-based and similarity-based reasoning. Artificial Intelligence. 75(2), 241-295.

[10] Bergmann, R., 2002. Experience management: Foundations, development methodology, and internet-based applications. Springer: Berlin.

[11] Russell, S., Norvig, P., 2020. Artificial intelligence: A modern approach (4th Edition). Prentice Hall: Upper Saddle River.

[12] Laudon, K.G., Laudon, K.C., 2020. Management information systems: Managing the digital firm (16th Edition). Pearson: Harlow.

[13] López-Robles, J.R., Otegi-Olaso, J.R., Gómez, I.P., et al., 2019. 30 years of intelligence models in management and business: A bibliometric review. International Journal of Information Management. 48, 22-38.

[14] Turing, A., 1950. Computing machinery and intelligence. Mind. 49, 433-460.

[15] Schwab, P.N., 2023. ChatGPT: 1000 Texts Analyzed and up to 75,3% Similarity [Internet] [cited 2023 Mar 17]. Available from: https://www.intotheminds.com/blog/en/chatgpt-similarity-with-plan/

[16] Sun, Z., Finnie, G., Weber, K., 2004. Case base building with similarity relations. Information Sciences. 165(1-2), 21-43.

[17] Finnie, G., Sun, Z., 2003. R5 model for case-based reasoning. Knowledge-Based Systems. 16(1), 59-65.

[18] Kantardzic, M., 2011. Data mining: Concepts, models, methods, and algorithms. John Wiley & Sons: Hoboken.

[19] Jordan, M.I., Mitchell, T.M., 2015. Machine learning: Trends, perspectives, and prospects. Science. 349(6245), 255-260.

[20] Epp, S.S., 2010. Discrete mathematics with applications. Cengage Learning: Boston.

[21] Zhang, D.G., Ni, C.H., Zhang, J., et al., 2022. New method of vehicle cooperative communication based on fuzzy logic and signaling game strategy. Future Generation Computer Systems. 142, 131-149.

[22] Finnie, G., Sun, Z., 2002. Similarity and metrics in case-based reasoning. International Journal of Intelligent Systems. 17(3), 273-287.

[23] Zhang, D., Wang, W., Zhang, J., et al., 2023. Novel edge caching approach based on multi-agent deep reinforcement learning for Internet of vehicles. IEEE Transactions on Intelligent Transportation Systems. 24(6), 1-16.

[24] Klawonn, F., Castro Peña, J.L., 1995. Similarity in fuzzy reasoning. Mathware & Soft Computing. 2(3), 197-228.

[25] Fontana, F.A., Formato, F., 2002. A similarity-based resolution rule. International Journal of Intelligent Systems. 17(9), 853-872.

[26] Biacino, L., Gerla, G., Ying, M., 2000. Approximate reasoning based on similarity. Mathematical Logic Quarterly. 46(1), 77-86.

[27] Kundu, S., 2000. Similarity relations, fuzzy linear orders, and fuzzy partial orders. Fuzzy Sets and Systems. 109(3), 419-428.

[28] Ovchinnikov, S., 1991. Similarity relations, fuzzy partitions, and fuzzy orderings. Fuzzy Sets and Systems. 40(1), 107-126.

[29] Bogacz, R., Giraud-Carrier, C., 2000. A novel modular neural architecture for rule-based and similarity-based reasoning. Hybrid neural systems. Springer: Berlin. pp. 63-77.

[30] Hüllermeier, E., 2001. Similarity-based inference as evidential reasoning. International Journal of Approximate Reasoning. 26(2), 67-100.

[31] Sun, Z., 2017. A logical approach to experience-based reasoning. New Mathematics and Natural Computation. 13(1), 21-40.

[32] Loia, V., Senatore, S., Sessa, M.I., 2004. Combining agent technology and similarity-based reasoning for targeted E-mail services. Fuzzy Sets and Systems. 145(1), 29-56.

[33] Sun, Z., Finnie, G., Weber, K., 2005. Abductive case-based reasoning. International Journal of Intelligent Systems. 20(9), 957-983.

[34] Magnani, L., 2011. Abduction, reason and science: Processes of discovery and explanation. Springer Science & Business Media: Berlin.

[35] Baral, C., 2000. Abductive reasoning through filtering. Artificial Intelligence. 120(1), 1-28.

[36] Console, L., Dupré, D.T., Torasso, P., 1991. On the relationship between abduction and deduction. Journal of Logic and Computation. 1(5), 661-690.

[37] Sun, Z., Finnie, G., Sun, J. (editors), 2005. Four new fuzzy inference rules for experience based reasoning. Fuzzy Logic, Soft Computing and Computational Intelligence (IFSA2005); 2005 May 30; Beijing. p. 188-193.

[38] Reeves, S., Clarke, M., 1990. Logic for computer science. Addison-Wesley: Wokingham.

[39] Hurley, P.J., 2000. A concise introduction to logic. Thomson Learning: Wadsworth.

[40] Dosen, K., 1993. A historical introduction to substructrual logics. Substructrual logics. Clarendon Press: Oxford. pp. 1-30.

[41] Sun, Z., 2022. Problem-based Computing and Analytics. International Journal of Future Computer and Communication.11(3), 52-60.

[42] Sun, Z., Stranieri, A., 2021. The nature of intelligent analytics. Intelligent analytics with advanced multi-industry applications. IGI-Global: Hershey. pp. 1-22.

[43] Sun, Z., Pambel, F., Wu, Z., 2022. The elements of intelligent business analytics: Principles, techniques, and tools. Handbook of research on foundations and applications of intelligent business analytics. IGI-Global: Hershey. pp. 1-20.

[44] Iantovics, L.B., Kountchev, R., Crișan, G.C., 2019. ExtrIntDetect—A new universal method for the identification of intelligent cooperative multiagent systems with extreme intelligence. Symmetry. 11(9), 1123.

[45] Sun, Z., Finnie, G., 2016. A Similarity Based Approach to Experience Based Reasoning (Prepprint). Available: https://ro.uow.edu.au/

[46] Sun, Z., Finnie, G., 2005. MEBRS: A multiagent architecture for an experience based reasoning system. Knowledge-based intelligent information and engineering systems. Springer: Berlin. pp. 972-978.

Downloads

How to Cite

Sun, Z. (2023). Similarity Intelligence: Similarity Based Reasoning, Computing, and Analytics. Journal of Computer Science Research, 5(3), 1–14. https://doi.org/10.30564/jcsr.v5i3.5575

Issue

Vol. 5 , Iss. 3 (July 2023)

Article Type

Article

License

Copyright © 2023 Author(s)

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC