Meta-learning Architectures for Adaptive AI Systems in Dynamic Environments

Akash Vijayrao Chaudhari

Meta-learning Architectures for Adaptive AI Systems in Dynamic Environments

Author(s)	Akash Vijayrao Chaudhari
Country	United Kingdom
Abstract	Efficient learning in dynamic and non-stationary changes is becoming critical in adaptive artificial intelligence (AI) systems in scientific, industrial and societal applications. Conventional machine-learning models often assume a fixed set of data distribution and fixed task design, thus limiting their application in the field when applied in real-world environments that are characterized by uncertainty, changing trends and situational diversity. The emergence of meta-learning, or learning to learn as it is often called, has already appeared as a ground-breaking paradigm by which AI systems can quickly learn to operate in new situations, and on new environments on the basis of prior experience. This paper, therefore, presents an in-depth analysis of meta-learning systems designed to be deployed with adaptive AI systems in changing milieus. Contemporary theoretical foundations, algorithmic developments, and architectural innovations in deep meta-learning, meta- reinforcement learning, adaptive hyperparameter optimization and continuous learning structures are synthesized in the paper. Studies that have been carried out in the past show that by using meta-learning processes, it becomes possible to adapt continuously in non-notation and competitive conditions by exploiting hierarchical knowledge transfer and fast parameter updating techniques. Additionally, meta-reinforcement learning methods have demonstrated strong features in real time adaptive control and dynamic decision making environments, leading to increasing the speed with which behavior changes and policy generalisation is achieved. In the article, they also examine the newer architectures including self-evolving neural networks, systems of fairness-sensitive online meta-learning, and system-specific continuous meta-learning applications designed in complex settings that feature heterogeneous data sources with changing goals. The evidence of the surveys shows that deep meta-learning strategies can be used to boost the learning efficiency, strength, and generality of application in various application areas, such as robotics, wireless sensing, enterprise decision systems, and adaptive communication networks.
Keywords	meta-learning, adaptive artificial intelligence, dynamic environments, meta-reinforcement learning, continuous learning architectures, autonomous systems.
Published In	Volume 4, Issue 5, May 2023
Published On	2023-05-12

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Meta-learning Architectures for Adaptive AI Systems in Dynamic Environments

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