Research on intelligent decision making algorithms for complex scenes incorporating knowledge retrieval and generation mechanisms

With the rapid development of science and technology, the application of intelligent decision-making systems in complex dynamic environments has become more and more widespread, and how to make effective decisions in such complex and uncertain scenarios has become an important research topic. The integration of knowledge retrieval and generation mechanism has become an effective means to enhance the capability of intelligent decision-making systems. In this paper, we propose an intelligent decision-making algorithm based on the fusion of knowledge retrieval and generation mechanism, aiming to solve the decision-making problem in complex scenarios. The algorithm generates an adaptive decision scheme by efficiently retrieving relevant knowledge and combining it with a generative mechanism, thus improving the accuracy and robustness of decision making. This paper first introduces the basic concepts and related methods of knowledge retrieval and generation mechanism, and then explores the idea of integrating the two in complex scenes. Then, this paper designs and implements the decision-making algorithm, and verifies its superiority in decision-making accuracy, response time and adaptability through experiments, which has significant advantages over traditional algorithms. Finally, this paper discusses the potential of the algorithm in practical applications and future research directions, especially in the fields of automatic driving, intelligent manufacturing and intelligent medical treatment.