REFERENCES

1. Zadeh LA. Fuzzy sets. Inf Contr 1965;8:338-53.

2. Mamdani EH, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mac Studies 1975;7:1-13.

3. Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. In: IEEE Trans Syst, Man, Cybern 1985;8: 116–32.

4. Precup RE, Hellendoorn H. A survey on industrial applications of fuzzy control. Comput Industry 2011;62:213-26.

5. Pirovano M. The use of fuzzy logic for artificial intelligence in games. University of Milano, Milano 2012. Available from: https://www.michelepirovano.com/pdf/fuzzy_ai_in_games.pdf [Last accessed on 6 Jun 2023].

6. Russell SJ. Artificial intelligence a modern approach. Pearson Education, Inc.; 2010.

7. Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of Go without human knowledge. Nature 2017;550:354-59.

8. Lakhani AI, Chowdhury MA, Lu Q. Stability-preserving automatic tuning of PID control with reinforcement learning. Complex Eng Syst 2022;2:3.

9. Li Y. Deep reinforcement learning: an overview. arXiv preprint arXiv: 170107274 2017.

10. Zhao E, Yan R, Li J, Li K, Xing J. AlphaHoldem: high-Performance artificial intelligence for heads-up no-limit poker via end-to-end reinforcement learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36; 2022. pp. 4689–97.

11. Shao K, Tang Z, Zhu Y, Li N, Zhao D. A survey of deep reinforcement learning in video games. arXiv preprint arXiv: 191210944 2019.

12. Chen J, Yuan B, Tomizuka M. Model-free deep reinforcement learning for urban autonomous driving. In: 2019 IEEE intelligent transportation systems conference (ITSC). IEEE; 2019. pp. 2765–71.

13. Walker O, Vanegas F, Gonzalez F, Koenig S. A deep reinforcement learning framework for UAV navigation in indoor environments. In: 2019 IEEE Aerospace Conference. IEEE; 2019. pp. 1–14.

14. Ouyang L, Wu J, Jiang X, et al. Training language models to follow instructions with human feedback. J Inf Process Syst 2022;35:27730-44.

15. Mundhenk TN, Chen BY, Friedland G. Efficient saliency maps for explainable AI. arXiv preprint arXiv: 191111293 2019.

16. Borys K, Schmitt YA, Nauta M, et al. Explainable AI in medical imaging: an overview for clinical practitioners–Beyond saliency-based XAI approaches. Eur J Radiol 2023:110786.

17. Holzinger A, Saranti A, Molnar C, Biecek P, Samek W. Explainable AI methods-a brief overview. In: xxAI-Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers. Springer; 2022. pp. 13–38.

18. Hagras H. Toward human-understandable, explainable AI. Computer 2018;51:28-36.

19. Mencar C, Alonso JM. Paving the way to explainable artificial intelligence with fuzzy modeling. In: Fuzzy Logic and Applications. Springer; 2019. pp. 215–27.

20. Viaña J, Ralescu S, Cohen K, Ralescu A, Kreinovich V. Extension to Multidimensional Problems of a Fuzzy-Based Explainable and Noise-Resilient Algorithm. In: Decision Making Under Uncertainty and Constraints: A Why-Book. Springer; 2023. pp. 289–96.

21. Pickering L, Cohen K. Genetic Fuzzy Controller for the Homicidal Chauffeur Differential Game. In: Applications of Fuzzy Techniques: Proceedings of the 2022 Annual Conference of the North American Fuzzy Information Processing Society NAFIPS 2022. Springer; 2022. pp. 196–204.

22. Pickering L, Cohen K. Toward explainable AI—genetic fuzzy systems—a use case. In: Explainable AI and Other Applications of Fuzzy Techniques: Proceedings of the 2021 Annual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2021. Springer; 2022. pp. 343–54.

23. Fernandez A, Herrera F, Cordon O, del Jesus MJ, Marcelloni F. Evolutionary fuzzy systems for explainable artificial intelligence: Why, when, what for, and where to? IEEE Comput Intell Mag 2019;14:69-81.

24. Berenji HR. A reinforcement learning—based architecture for fuzzy logic control. Int J Approx Reason 1992;6:267-92.

25. Glorennec PY, Jouffe L. Fuzzy Q-learning. In: Proceedings of 6th international fuzzy systems conference. vol. 2. IEEE; 1997. pp. 659–62.

26. Er MJ, Deng C. Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning. IEEE Trans Syst Man Cybern B Cybern 2004;34:1478-89.

27. Jamshidi P, Sharifloo AM, Pahl C, Metzger A, Estrada G. Self-learning cloud controllers: Fuzzy q-learning for knowledge evolution. In: 2015 International Conference on Cloud and Autonomic Computing. IEEE; 2015. pp. 208–11.

28. Kumar S. Learning of Takagi-Sugeno Fuzzy Systems using Temporal Difference methods. DigiPen Institute of Technology; 2020.

29. Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning. Nature 2015;518:529-33.

30. Jang JSR. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst, Man, Cybern 1993;23:665-85.

31. King SD. Explainable AI competition; 2023. Accessed on 2/15/2023. Available from: https://xfuzzycomp.github.io/XFC/. [Last accessed on 6 Jun 2023].

32. Sugeno M, Kang G. Structure identification of fuzzy model. Fuzzy Sets Syst 1988;28:15-33.

33. Bellman R. A markovian decision process. Indiana Univ Math J 1957;6: 679–84. Available from: https://www.jstor.org/stable/24900506 [Last accessed on 6 Jun 2023].

34. Watkins CJCH, Dayan P. Q-learning. Mach Learn 1992;8:279-92.

35. Vinyals O, Babuschkin I, Czarnecki WM, et al. Grandmaster level in StarCraft Ⅱ using multi-agent reinforcement learning. Nature 2019;575:350-54.

36. Lin LJ. Self-improving reactive agents based on reinforcement learning, planning and teaching. Mach Learn 1992;8:293-321.

37. Schaul T, Quan J, Antonoglou I, Silver D. Prioritized experience replay. arXiv; 2015.

38. Andrychowicz M, Wolski F, Ray A, et al. Hindsight Experience Replay. arXiv; 2018.

39. van Hasselt H, Guez A, Silver D. Deep reinforcement learning with double Q-learning. arXiv; 2015.

40. Polyak BT, Juditsky AB. Acceleration of Stochastic Approximation by Averaging. SIAM J Control Optim 1992;30:838-55.

41. Rosenstein MT, Barto AG, Si J, et al. Supervised actor-critic reinforcement learning. Learning and Approximate Dynamic Programming: Scaling Up to the Real World 2004: 359–80. Available from: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=ec4de9c9729f9301aefa713ac42f194a364a4406 [Last accessed on 6 Jun 2023].

42. Yan X, Deng Z, Sun Z. Competitive Takagi-Sugeno fuzzy reinforcement learning. In: Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01)(Cat. No. 01CH37204). IEEE; 2001. pp. 878–83.

43. Jaakkola T, Jordan M, Singh S. Convergence of stochastic iterative dynamic programming algorithms. Adv Neural Inf Proces Syst 1993;6. Available from: https://proceedings.neurips.cc/paper/1993/file/5807a685d1a9ab3b599035bc566ce2b9-Paper.pdf [Last accessed on 6 Jun 2023].

44. Melo FS. Convergence of Q-learning: a simple proof. Inst Syst Robot, Tech Rep 2001;1–4. Available from: https://d1wqtxts1xzle7.cloudfront.net/55970511/ProofQlearning-libre.pdf?1520268288=&response-content-disposition=inline%3B+filename%3DConvergence_of_Q_learning_a_simple_proof.pdf&Expires=1686128725&Signature=SZMvoQSM3Z7UPmyXfT4QOw8Co0pUvQM1h3NfUwa3aJXBPsj8ox1O9WI~QaTZrpZ5~Cr4NcfcDmsh~IUjT101xNeKR2-PCvewznfNXB38~UEGN736l3lniIQLKe1QdebMTHgvtL7iDOivntOKxrLAnzUx0I4dYlAuYUf3qBNk37aqJtIH6WTpCuJUKeH3pW282tY11MVEK0P~Czp-WsOkY8wtMOu8~NCNcS2sR6d1rhV1JeWPv1BuTAg6-hBUFFhbqLlY7SvJ8j6IWA0bJy~Miaz4Q2C37sOi2eo2~y819e3F3jiby3mMWeEpf1WYPUWK~0hB475dafC5FGZcEKTVjA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA [Last accessed on 6 Jun 2023].

45. Cybenko G. Approximation by superpositions of a sigmoidal function. Math Control Signal Systems 1989;2:303-14.

46. Yarotsky D. Error bounds for approximations with deep ReLU networks. Neural Netw 2017;94:103-14.

47. Melo FS, Ribeiro MI. Q-learning with linear function approximation. In: Learning Theory: 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA; June 13-15, 2007. Proceedings 20. Springer; 2007. pp. 308–22.

48. Papavassiliou VA, Russell S. Convergence of reinforcement learning with general function approximators. In: IJCAI. vol. 99; 1999. pp. 748–55. Available from: http://people.eecs.berkeley.edu/~russell/papers/ijcai99-bridge.pdf [Last accessed on 6 Jun 2023].

49. Ying H. General Takagi-Sugeno fuzzy systems are universal approximators. In: 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98CH36228). vol. 1. IEEE; 1998. pp. 819–23.

50. Bede B. Mathematics of Fuzzy Sets and Fuzzy Logic. Springer; 2013.

51. Brockman G, Cheung V, Pettersson L, et al. Openai gym. arXiv preprint arXiv: 160601540 2016.

52. Benmiloud T. Multioutput Adaptive Neuro-Fuzzy Inference System. In: Proceedings of the 11th WSEAS International Conference on Nural Networks and 11th WSEAS International Conference on Evolutionary Computing and 11th WSEAS International Conference on Fuzzy Systems. NN'10/EC'10/FS'10. Stevens Point, Wisconsin, USA: World Scientific and Engineering Academy and Society (WSEAS); 2010. p. 94–98.

53. Al-Hmouz A, Shen J, Al-Hmouz R, Yan J. Modeling and simulation of an adaptive neuro-Fuzzy inference system (ANFIS) for mobile learning. IEEE Trans Learning Technol 2012;5:226-37.

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