A Systematic Review of Context Reasoning Approaches for Visual Language and IoT Data Analysis in Artificial Intelligence

Abstract

This paper provides a systematic review of context reasoning approaches in artificial intelligence (AI), focusing on advancements in visual language and IoT data interpretation. Contextual reasoning, which integrates domain knowledge, situational awareness, and real-time IoT data, is vital for developing AI systems capable of human-like decision-making. Despite significant progress in deep learning, current models often struggle to effectively handle context-dependent tasks, such as visual reasoning, inductive learning, commonsense language comprehension, and dynamic IoT environments. Hybrid methodologies, combining neural and symbolic reasoning, have emerged as promising solutions, yet challenges remain in scalability, generalization, and explainability. This review highlights recent developments in neuro-symbolic integration, context-aware meta-reinforcement learning, and abductive learning, addressing their strengths and limitations. We also examine new benchmarks designed to emulate human cognition and IoT-driven scenarios, revealing gaps in current models' ability to achieve human-level reasoning. Finally, we discuss future directions, emphasizing the need for dynamic, adaptive systems that leverage hybrid approaches, enhanced inductive reasoning, IoT integration, and comprehensive empirical evaluation methods to close the gap between machine learning and human cognitive abilities.