Re-Exploring Intelligent Systems: Reasoning, Learning, and Control Through the Lens of RLC Circuits

Yıl 2024, Cilt: 1 Sayı: 1, 1 - 5, 20.07.2024

Tufan Kumbasar

Öz

This paper explores the realm of intelligent systems through an analogy inspired by RLC circuits, delving into the interconnected dynamics of reasoning, learning, and control. Leveraging the simplicity and clarity of the analogy, we navigate the conceptual landscape, drawing parallels between electrical components and the cognitive functions of modern AI. The presented analogical framework is the conclusion of the personal experiences of the author in developing intelligent systems, sparked by conversations with fellow researchers and students and presentations of research outcomes. It is worth recognizing the limitations of this analogy, as its reductionist nature may oversimplify the complexities inherent in intelligent systems. However, this exploration provides a fresh perspective on the foundational components of intelligent systems through the lens of the well-established RLC circuit theory.

Anahtar Kelimeler

Artificial intelligence, intelligent systems, RLC, analogy

Teşekkür

The author acknowledges the insightful discussions and conceptual refinement achieved through interactions with ChatGPT 3.5 and Microsoft Co-pilot.

Kaynakça

• I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016.
• Q. Zhang, L. T. Yang, Z. Chen, and P. Li, “A survey on deep learning for big data,” Information Fusion, vol. 42, pp. 146–157, 2018.
• G. Mialon, R. Dessı, M. Lomeli, et al., “Augmented language models: A survey,” arXiv preprint arXiv:2302.07842, 2023.
• L. Floridi and M. Chiriatti, “Gpt-3: Its nature, scope, limits, and consequences,” Minds and Machines, vol. 30, pp. 681–694, 2020.
• H. Hagras, “Towards true explainable artificial intelligence for real world applications,” 2023.
• W. Samek and K.-R. Müller, “Towards explainable artificial intelligence,” Explainable AI: interpreting, explaining and visualizing deep learning, pp. 5–22,2019.
• R. Goebel, A. Chander, K. Holzinger, et al., “Explainable ai: The new 42?” In Machine Learning and Knowledge Extraction, Springer, 2018, pp. 295–303.
• S. J. Russell and P. Norvig, Artificial intelligence a modern approach. London, 2010.
• C. Grosan and A. Abraham, Intelligent systems. Springer, 2011, vol. 17.
• M. Negnevitsky, Artificial intelligence: a guide to intelligent systems. Pearson education, 2005.
• K. Fu, “Learning control systems and intelligent control systems: An intersection of artifical intelligence and automatic control,” IEEE Transactions on Automatic Control, vol. 16, no. 1, pp. 70–72, 1971.
• K. S. Narendra, “Intelligent control,” IEEE Control Systems Magazine, vol. 11, no. 1, pp. 39–40, 1991.
• K. J. Åström and T. J. McAvoy, “Intelligent control,” Journal of Process control, vol. 2, no. 3, pp. 115–127, 1992.
• P. J. Antsaklis, “Intelligent control,” Encyclopedia of Electrical and Electronics Engineering, vol. 10, pp. 493–503, 1997.
• K. M. Passino, Intelligent control: An overview of techniques, 2001.
• G. Stephanopoulos and C. Han, “Intelligent systems in process engineering: A review,” Computers & Chemical Engineering, vol. 20, no. 6-7, pp. 743–791, 1996.
• I. J. Rudas and J. Fodor, “Intelligent systems,” International Journal of Computers, Communications & Control, vol. 3, no. 3, pp. 132–138, 2008.
• J. M. Garibaldi, “The need for fuzzy ai,” IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 3, pp. 610–622, 2019.
• R. R. Yager and L. A. Zadeh, An introduction to fuzzy logic applications in intelligent systems. Springer Science & Business Media, 2012, vol. 165.
• R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction. MIT press, 2018.
• P. J. Antsaklis, K. M. Passino, and S. Wang, “Towards intelligent autonomous control systems: Architecture and fundamental issues,” Journal of Intelligent and Robotic Systems, vol. 1, pp. 315–342, 1989.
• J. D. Irwin and R. M. Nelms, Basic engineering circuit analysis. John Wiley & Sons, 2020.