‎Role of Fuzzy Sets on Artificial Intelligence Methods‎: ‎A literature Review

الموضوعات : Transactions on Fuzzy Sets and Systems

Cengiz Kahraman ¹ , Sezi Onar ² , Basar Oztaysi ³ , Selcuk Cebi ⁴

1 - Department of Industrial Engineering, IStanbul Technical University, Istanbul, Turkey.
2 - Department of Industrial Engineering, IStanbul Technical University, Istanbul, Turkey.
3 - Department of Industrial Engineering, IStanbul Technical University Istanbul, Turkey.
4 - Department of Industrial Engineering, Yildiz Technical University, Istanbul, Turkey.

تاريخ الإرسال : 05 الخميس , جمادى الثانية, 1444 تاريخ التأكيد : 13 الأحد , شعبان, 1444 تاريخ الإصدار : 17 الأحد , شوال, 1444

الکلمات المفتاحية: Artificial intelligence, Fuzzy sets, Automated reasoning, Autonomous agents, Machine learning, Deep learning, Information reasoning, Neural networks,

ملخص المقالة :

Machines can model and improve the human minds capabilities through artificial intelligence. One of the most popular tools of artificial intelligence is fuzzy sets, which can capture and model the vagueness and impreciseness in human thoughts. This paper, first of all, introduces the recent extensions of ordinary fuzzy sets and then presents a literature review on the integration of fuzzy sets with other artificial intelligence techniques such as automated reasoning, autonomous agents, multi-agent systems, machine learning, case-based reasoning, deep learning, information reasoning, information representation, natural language processing, symbolic reasoning, and neural networks. Graphical illustrations of literature review results are presented for each of these integrated artificial intelligence techniques. The results of a patent search on fuzzy artificial intelligence are also given.

المصادر:

[1] K. -P. Adlassnig and G. Kolarz, Representation and semiautomatic acquisition of medical knowledge in CADIAG-1 and CADIAG-2, Computers and Biomedical Research, 19(1) (1986), 63-79.
[2] J. C. R. Alcantud, Ranked hesitant fuzzy sets for multi-criteria multi-agent decisions, Expert Systems with Applications, 209, art. no. 118276, (2022).
[3] K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets System, 20(1) (1986), 8796.
[4] K. Atanassov, Intuitionistic fuzzy sets, theory and applications, Newyork: Heidelberg: Physica-Verlag, (1999).
[5] A. Bonarini, Evolutionary learning of general fuzzy rules with biased evaluation functions: Competition and cooperation, IEEE Conference on Evolutionary Computation - Proceedings, 1 (1994), 51-56.
[6] M. Bonnie, F. Maryanski, Knowledge base for code reuse by similarity, Proceedings - IEEE Computer Society’s International Computer Software & Applications Conference, (1989), 634-641.
[7] R. Brachman and H. Levesque, Knowledge Representation and Reasoning, Morgan Kaufmann, (2004), ISBN: 9780080489322.
[8] S. Cerutti and C. TimPieri, A method for the quantification of the decision-making process in a computer-oriented medical world, International Journal of Bio-Medical Computing, 12(1) (1981), 29-57.
[9] X. Chen, W. Zhang, X. Xu and W. Cao, Managing Group Confidence and Consensus in Intuitionistic Fuzzy Large Group Decision-Making Based on Social Media Data Mining, Group Decision and Negotiation, 31(5) (2022), 995-1023.
[10] C. Chiu, P. -L. Hsu, A. F. Norcio, Fuzzy reasoning approach to support knowledge acquisition, International Journal of General Systems, 28(2) (1999), 227-241.
[11] P. Cordero, M. Enciso, D. Lpez-Rodrguez and . Mora, Formal Concept Analysis with R, R Journal, 14(1) (2022), 341-360.
[12] B. Coung, Picture fuzzy sets, Journal of Computer Science and Cybernetics, 30(4) (2014), 409420.
[13] R. Davis, H. Shrobe and P. Szolovits, What is a Knowledge Representation? AI Magazine, 14(1) (1993), 17-33.
[14] P. Frederic, Automated Reasoning, The Stanford Encyclopedia of Philosophy (Fall 2021 Edition), Edward N. Zalta (ed.), URL = ¡https://plato.stanford.edu/archives/fall2021/entries/reasoning-automated/¿.
[15] Y. Gao, R. Bao, Z. Pan, G. Ma, J. Li, X. Cai and Q. Peng, Mechanical equipment health management method based on improved intuitionistic fuzzy entropy and case reasoning technology, Engineering Applications of Artificial Intelligence, 116 (2022), art. no. 105372.
[16] F. Hoitsma, A. Knoben, M. L. Espinosa and G. Npoles, Symbolic explanation module for fuzzy cognitive map-based reasoning models, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12498 LNAI, (2020), 21-34.
[17] H. Hu, L. Pang and Z. Shi, Image matting in the perception granular deep learning, Knowledge-Based Systems, (2016), 102, 51-63.
[18] C. Kahraman and F. Kutlu Gndogdu, From 1D to 3D membership: spherical fuzzy sets, BOS / SOR 2018, Polish Operational and Systems Research Society, September 24th 26th 2018, Palais Staszic, Warsaw, Poland.
[19] A. Kandel and M. Schneider, Fuzzy Sets and Their Applications to Artificial Intelligence, Editor(s): Marshall C. Yovits, 
C. Kahraman, S. Cevik Onar, B. Oztaysi, S. Cebi-TFSS-Vol.2, No.1-(2023)
[20] F. Kutlu Gndodu and C. Kahraman, Spherical fuzzy sets and spherical fuzzy TOPSIS method, Journal of Intelligent & Fuzzy Systems, 36(1) (2019), 337-352.
[21] S. C. Lee and E. T. Lee, Fuzzy sets and neural networks, Journal of Cybernetics, 4 (2) (1974), 83-10.
[22] J. Liu, Autonomous Agents and Multi-Agent Systems: Explorations in Learning, Self-Organization and Adaptive Computation, World Scientific Publishing Company, (2001), ISBN: 9810242824.
[23] Y. Liu, J. Zhao, L. Wang and W. Wang, Unified Modeling for Multiple-Energy Coupling Device of Industrial Integrated Energy System, IEEE Transactions on Industrial Electronics, 70(1) (2023), 1005-1015.
[24] H. Parveen, S. W. A. Rizvi and P. K. Shukla, Disease risk level prediction based on knowledge driven optimized deep ensemble framework, Biomedical Signal Processing and Control, 79 (2023), art. no. 103991.
[25] J. Pascual-Fontanilles, A. Valls, A. Moreno and P. Romero-Aroca, Continuous Dynamic Update of Fuzzy Random Forests, International Journal of Computational Intelligence Systems, 15(1) (2022), art. no. 74.
[26] T. J. Ross, F. S. Wong, S. J. Savage and H. C. Sorensen, Daps: An Expert System for Damage Assessment of Protective Structures, (1986), 109-120.
[27] S. Shiu and S. K. Pal, Foundations of Soft Case-Based Reasoning, John Wiley & Sons, (2004), ISBN: 9780471086352.
[28] H. Singh, M. M. Gupta, T. Meitzler, Z. G. Hou, K. K. Garg, A. M. G. Solo and L. A. Zadeh, Real-Life Applications of Fuzzy Logic, Advances in Fuzzy Systems, (2013).
[29] F. Smarandache, Neutrosophy: neutrosophic probability, set, and logic: analytic synthesis & synthetic analysis, American Research Press, (1998).
[30] V. Torra, Hesitant fuzzy sets, International Journal of Intelligent Systems, 25(6) (2010), 529-539.
[31] F. Valdez and O, Castillo and P. Melin, Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering, Algorithms (2021), 14, 122.
[32] M. Watanabe, From Biological to Artificial Consciousness: Neuroscientific Insights and Progress,
Springer International Publishing, (2022), ISBN: 9783030911386
[33] R. Yager, Pythagorean fuzzy subsets, in Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, (2013).
[34] R. Yager, Generalized orthopair fuzzy sets, IEEE Transactions on Fuzzy Systems, 25(5) (2017), 1222-1230.
[35] W. Yue, X. Wan, S. Li, H. Ren and H. He, Simplified Neutrosophic Petri Nets Used for Identification of Superheat Degree, International Journal of Fuzzy Systems, (2022).
[36] L. A. Zadeh, Fuzzy set, Information and Control, 8(3) (1965), 338-353.
[37] L. A. Zadeh, The concept of a linguistic variable and its application, Information Sciences, 8(3) (1975), 199-249.
[38] L. A. Zadeh, Fuzzy sets as a basis for a theory of possibility, fuzzy sets and systems, 1(1) (1978), 3-28.
[39] L. A. Zadeh, The role of fuzzy logic in the management of uncertainty in expert systems fuzzy sets and systems, 11 (1-3) (1983), 199-227.
[40] L. A. Zadeh, Commonsense knowledge representation based on fuzzy logic, Computer, 16 (10) (1983), 61-65.
[41] C. Zhu and Z. Wang, Fuzzy comprehensive evaluation-based artificial consciousness model, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), art. no. 5554447, (2010), 1594-1598.