Robot Intelligent Decision-making: From Rule-based Systems to Deep Learning and Multi-agent Methods

Abstract

The integration of computer vision and intelligent decision systems has completely changed the field of robot technology, enabling autonomous systems to perceive, reason and perform actions in complex environments. This paper reviews the evolution of robot decision-making methods from traditional rule-based methods to contemporary deep learning and Multi-Agent Reinforcement learning paradigms. Three types of decision-making frameworks are systematically analyzed: rule driven systems that rely on predefined logical conditions, deep learning methods that use neural networks for end-to-end strategy learning, and multi-agent systems that coordinate collective behavior through distributed intelligence. The results show that although the rule-based method provides interpretability and security, the deep learning method performs well in dealing with high-dimensional sensory input and an unstructured environment. In addition, Multi-Agent Reinforcement Learning shows great potential in the application of cooperative robots. This review identifies the current limitations, including sample efficiency, generalization ability and real-time performance constraints, and emphasizes future research directions such as hybrid architectures that combine the advantages of different methods.