METHODS OF TRAINING AND ADAPTATION OF AI AGENTS IN COMPLEX PROCESS CONTROL SYSTEMS

Аннотация

INTRODUCTION prominence in addressing these challenges, Modern control systems are becoming increasingly offering adaptive solutions that can learn complex and multifaceted, necessitating continual autonomously, analyze data, and make decisions adaptation to changing conditions and the ability based on the current situation. Among various AI to operate effectively under high uncertainty. In approaches, particular emphasis is placed on such environments, traditional automation methods for training and adapting AI agents, which approaches prove insufficiently flexible, as they provide the flexibility and autonomy demanded by struggle to respond appropriately to unpredictable complex control systems. changes in processes and external factors. In recent years, artificial intelligence (AI) and The relevance of this study is underscored by the machine learning have gained considerable growing need for adaptive and intelligent systems https://www.theamericanjournals.com/index.php/tajet 46 THE USA JOURNALS THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN – 2689-0984) VOLUME 06 ISSUE11 capable of efficient operation in industries such as efficiency and reduce costs. Azeem M. et al. [9] note manufacturing, energy, transportation, and other that in the manufacturing sector, the use of big data sectors that require rapid responses to change. addresses significant challenges and improves Applying machine learning techniques, such as production efficiency. The practical aspect of this neuroevolution and reinforcement learning, application was considered in this study through allows AI agents to learn unique process the example of Volkswagen [10]. Additionally, the characteristics and adapt to new conditions in real practical side of AI applications was explored time, significantly enhancing their effectiveness. based on Tesla's experience [12]. In aviation, AI contributes to improving aircraft fuel efficiency Despite notable progress in AI, integrating through advanced technologies, supporting the trainable AI agents into complex control systems industry's sustainable development [11]. remains a challenging task. The need for continuous learning and adaptation of agents to Soori M., Arezoo B., and Dastres R. [7] observe that dynamic conditions, along with the ability to act in robotics, the integration of AI, machine learning, based on large-scale data analysis, demands the and deep learning is transforming robot development and testing of new methods. Current capabilities, making them more complex and research indicates that approaches like intelligent. Galván E. and Mooney P. [5] believe that reinforcement learning and deep neural networks these advancements are complemented by enable the creation of systems that not only adapt research in the neuroevolution of deep neural to changes but also continuously improve their networks, which enhances training algorithms and performance during operation. Consequently, addresses future challenges in AI systems. exploring methods for training and adapting AI Heuillet A., Couthouis F., and Díaz-Rodríguez N. [6] agents is an essential step in developing fully discuss the necessity for transparent and autonomous control systems capable of efficient interpretable AI models, especially in functioning in complex environments. reinforcement learning, where understanding AI The objective of this study is to investigate and decision-making processes builds trust and analyze contemporary methods for training and promotes broader adoption of the technology. adapting AI agents in control systems managing Practical applications are developing automated complex processes, as well as to assess their machine learning approaches for real-time fault effectiveness in ensuring reliability, resilience, and detection and diagnosis, which improve system high performance. reliability and maintenance, as noted by Leite D. et al. [8]. METHODS Beyond technical achievements, social aspects of This study employed comparative analysis, AI are also subjects of research. De Togni G. et al. systematization, synthesis, and examination of [3] examine emotional and relational aspects of AI practical examples that illustrate the use of systems to make them more "intelligent" and training and subsequent adaptation methods for AI "caring." Moradbakhti L., Schreibelmayr S., and agents in control systems for complex processes. Mara M. [4] explore gender dynamics in AI through Thon C. et al. [1] point out that artificial intelligence the lens of voice assistants, investigating how these (AI) plays a crucial role in various fields, technologies meet users' basic psychological particularly in process engineering, where it needs. The evolution and future of conversational optimizes complex industrial processes to enhance agents are analyzed with a proposed research https://www.theamericanjournals.com/index.php/tajet 47 THE USA JOURNALS THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN – 2689-0984) VOLUME 06