基于Agent的专家系统推理模型

针对传统专家系统推理模型结构在知识获取方面适应性差的现状，从系统科学的视角，运用复杂适应系统理论，对传统专家系统的结构及运行机制进行了改进.引入Agent来模拟人脑中的神经元，用来承载专家系统中相互作用的知识，然后，基于Multi-Agent之间的相互作用来构建复杂适应的专家系统推理模型.从而，将专家系统中的知识获取机制、知识库、推理机三者统一于由Multi-Agent进行相互作用的复杂适应系统之中.通过设计体育赛事申办决策专家系统的原型，进行了专家系统推理模型的验证.原型运行结果表明:基于Multi-Agent的专家系统推理模型结构能够有效地提高专家系统知识获取的适应性.这为研究更加接近人脑智能的专家系统提供了崭新的研究思路.

Research on the expert system reasoning model based on Agent

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.