Research And Application Of The Agent Optimization Analysis In FKAOS

As a more abstract modeling technology, Agent-oriented Requirement Engineering poses a challenge to the traditional software requirement analyzing method, yet meanwhile it brings about improvement and development. In traditional requirement analyzing technology (object-oriented and structure-oriented), the requirement analyzing step and the program design step are often out of joint. Due to the highly abstract nature of Agent itself, Agent-oriented program design agrees with the abstract description in the requirement analysis. Therefore the Agent-based requirement analyzing step and the detail design step can be combined seamlessly and supplement each other. The Agent-oriented requirement analysis aims to acquire the system basic component (the Agent object), and analyze the system-service-required cooperation. The results of the requirement analysis will become the analysis object of the following Agent-oriented detail program design step.While incorporating the goal-oriented KAOS method, this thesis puts forward an Agent-centered requirement analysis method—FKAOS, in which most energy is spent to acquire and optimize the Agent object. Using goal-oriented method, we can acquire some rough Agent objects, which, however, cannot fully reflect Agent's features. Through asking "why" these Agents are needed, this thesis analyzes these rough Agent objects and optimizes Agent design. The purpose of optimizing Agent objects is to build a cooperative social system, in which Agent is moderate sized, has its relatively independent tasks and its automatic features are fully reflected.This thesis includes three methods of optimizing Agent: the responsibility ontology-based, the requirement resource-based and the interactive relation-based. The focus of the responsibility ontology-based optimizing method is to optimize Agent object in the abstract responsibility aspect. The detailed responsibility ontology definition turned into abstract definition, Agent objects will become more abstract and the Agents are therefore optimized. Since Agent's requirement resource information and Agent's responsibility are closely connected, the resource-based optimizing analysis method is to analyze the resource information of Agent responsibility, then judge the similarity of Agent objects' responsibility, and finally optimize the Agent objects that have similar responsibility. Different from the other two Agent optimizing methods, the interactive relation-based method is to analyze Agent objects' external interactive features, exclude the redundant interactive actions and therefore efficiently simplify the cooperative social system of the multi-Agent system. The optimizing Agent is a iterative process, the responsibility ontology-based is most important method, the requirement resource-based is a necessary supplementary, and the interactive relation-based both a supplementary and a test.Finally, with the example (agricultural projects reporting subsystem), we made further elaborated this program. And through the example of this analysis, the program proved the effectiveness and operability.