ARTIFICIAL INTELLIGENCE-BASED DECISION-MAKING PROCESSES IN ROBOTICS A COMPREHENSIVE SCIENTIFIC REVIEW
Keywords:
artificial intelligence, robotics, decision-making, machine learning, deep reinforcement learning, autonomous systems, neural networks, probabilistic reasoningAbstract
The integration of artificial intelligence (AI) into robotic systems has fundamentally transformed the landscape of automated decision-making. This paper presents a comprehensive analysis of AI-based decision-making processes in robotics, examining key algorithms, architectures, and real-world applications across multiple domains. We investigate how machine learning, deep reinforcement learning, probabilistic reasoning, and hybrid AI approaches enable robots to perceive their environment, process information, and execute autonomous decisions with increasing accuracy and reliability. Through statistical analysis of performance benchmarks, comparative studies of decision architectures, and examination of industry deployment data, we demonstrate that AI-powered robots achieve decision accuracy rates of 87–96% in structured environments and 71–84% in dynamic, unstructured settings [1][2]. The paper also addresses ethical dimensions, safety constraints, and future research trajectories. Our findings indicate that multi-modal AI fusion represents the most promising pathway toward robust autonomous decision-making in next-generation robotic systems [3].
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