Self-Adaptive Artificial Intelligence Systems for Large-Scale Data-Driven Decision Making
Md Jubayar Hossain, Mohammed Majid Bakhsh, Mohammad Abdus Sami, Ishtiaque Alam, Md Mehedi Hassan Melon, Mia Md Tofayel Gonee Manik
Abstract
The growing magnitude, diversity, and speed of data produced across contemporary cyber-physical, enterprise, and cloud-edge systems has revealed inherent constraints with the inflexible artificial intelligence (AI) models regarding providing real-time choices. Self-adaptive artificial intelligence systems have become an attractive paradigm to overcome these challenges through enabling continuous learning, dynamic model reconfiguration and context-specific optimization in the changing data distributions and operational constraints. In this paper, a detailed self-adaptive AI architecture to serve large-scale decision making with data is provided, which involves online learning, feedback-based adjustment, and automatic policy optimization. The suggested architecture takes advantage of the hybrid intelligence methods merging deep learning, reinforcement learning, and meta-learning to reach strong performance in non-stationary and uncertain environments. Adaptive control loops are incorporated to check data drift, system performance and the consumption of resources and recalibration of a model can be done in real time without involving human intervention. The comprehensive test on large-scale datasets indicates that the proposed solution is much more accurate in making decisions, flexible and resilient to systems against the traditional non-adaptable and semi-adaptive AI models. The outcomes confirm the effectiveness of self-adaptive intelligence in aiding scalable, dependable, and explainable decision-making of dynamic data-intensive applications including smart infrastructure, autonomous systems, and intelligent business analytics.