Research on the expert system reasoning model based on Agent
Zhongzhi Shi · Caai Transactions on Intelligent Systems · 2013
The traditional expert system reasoning model structure has poor adaptability in acquiring knowledge. From the viewpoint of system science, the complex adaptive system theory is used to improve the structure and operation mechanism of a traditional expert system. Firstly, an Agent was introduced to simulate neurons in the human brain and load the knowledge interacting in the expert system reasoning model. Then an expert system reasoning model of complex adaptation was constructed based on the Multi-Agent interaction. Consequently, the knowledge acquiring mechanism, knowledge base and reasoning engine were unified into the Agents interaction in the complex adaptive expert system. Finally, by designing the expert system reasoning model prototype in decision-making of international sporting events bidding, the effectiveness of the expert system reasoning model based on Agent was verified. The results of the prototype running show that the expert system reasoning structure based on Multi-Agent model can effectively improve the adaptability of expert system knowledge acquisition. That provides a new idea for studying the expert system closer to human intelligence.