Integrating Machine Learning, Deep Learning and Robotics for Intelligent Information Management and Decision Support
Year: 2026
Type: article
Abstract: Machine Learning, Deep Learning, and Robotics are revolutionizing the landscape of intelligent information management and decision support, connecting data processing, predictive analysis, and autonomous action. This review discusses the conceptual background, technological architectures, and application areas of integrated intelligent systems. It brings together the latest advances in machine learning, deep learning, robotic perception, autonomous control, reinforcement learning, digital twins, edge AI, federated learning and human-in-the-loop decision support. The review also compares the machine learning, deep learning, robotics, and integrated approaches in terms of accuracy, scalability, adaptability, computational efficiency, explainability, and real-time decision making. These technologies are increasingly becoming relevant in the healthcare sector, in manufacturing, in supply chains, in the finance sector, in cybersecurity, in smart cities, in agriculture, and in environmental monitoring. While significant strides have been made, there are still numerous challenges to address, such as data quality, privacy, security, interoperability, computational complexity, transparency, and trust. Multimodal intelligence, foundation models, Distributed learning, and Self-learning robotics are expected to become increasingly common in future intelligent systems. In summary, learning, reasoning, and robotic execution, when integrated, provide a strong basis for trustworthy, adaptive, explainable and human-centric decision support in real-world complex environments and retain the effectiveness of human involvement in the decision process and the responsibility of system governance.
Source: Computer and decision making.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7225060782
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