HYBRID DEEP LEARNING FRAMEWORKS FOR ADAPTIVE DECISION-MAKING IN COMPLEX SYSTEMS

Abstract

Complex systems operate under conditions of uncertainty, high dimensionality, dynamic interactions, and multiple operational constraints. These domains require adaptable structures that are capable of learning on non-stationary data, adapting to non-stationary condition, imposing security and regulatory policies, and real-world problems, making use of latency, privacy, and computational constraints. Hybrid deep learning models, which involve a combination of different learning paradigms, including deep neural networks, reinforcement learning, symbolic reasoning, distributed learning, and optimization methods, are needed to work towards these problems in a holistic manner. The present paper is a critical discussion of hybrid deep learning models to adaptive decision-making in complex systems. A structured system is described based on the perception, symbolic knowledge representation, adaptive decision control, federated learning and optimization layers. The empirical study of exemplary areas of the complex-systems demonstrates an increased adaptability, dependability, adherence to safety rules, and efficiency in communication combined with readability in contrast to monolithic approaches to learning. The findings summarize the suitability of hybrid structures that should be employed in the safety-critical and data-sensitive systems.