Research On Intelligent Network Teaching System Based On Multi-Agent

The system of Web-based distance instruction is a sort of Internet-based distance education. This instructional mode can provides learner with a huge repository of knowledge and information which can stimulate learners to learn; so that learners can construct knowledge depending on their own initiatively and get, renew, even innovate knowledge by themselves, those aims belongs to our dreamboat.But there still are many problems in the online instruction system nowadays, first, the functions of active interaction in existing instruction system are not strong enough, and most of them are given mainly to present the instruction materials. Second, the system's intelligence is lower. Three, the system is lack of personalization. Four, there is not an effective guide to students in existing system.In view of defects above, the author of this thesis designed a new kind of personalized system of distance education, which is based on Agent technology. So it has got rid of the lower intelligence defects of existing system effectively.This paper proposes an agent-based distance tutoring system model. The model is made up of several agents. Utilizing the cooperative mechanism among agents, the model realizes knowledge exchange and share among the modules and shapes a lay-structure multi-agent system. Utilizing agent's intelligent reasoning ability, the model can learn characteristics of student's personality, adaptively build a sort of teaching strategy which adapts to student, intelligently organize teaching process and lead student to study better and to exert more potential. In addition, the designing ideas of the model not only bring a leading role of teacher into play but also fully reflect cognitive main body of student.The paper puts forward a multi-agent student model. The model involves the traits from four aspects of student and each aspect has an agent to reason its traits. By adopting concept map and fuzzy theory, the author raises a new kind of reasoning way for every agent in the student model. These ways not only hold relatively good reasoning ability but also are easily realized.Auto--generating paper is a constrained multi-object optimization problem. This paper presents a way based on genetic algorithm (GA) to solve the problem. We define the crossover operator and mutation operator in the real coded and adjust the probabilities of crossover and mutation. The main parameters in genetic algorithm can then be adjusted automatically according to individual fitness. Experiments show that this kind of genetic algorithm could solve auto-generating paper problems more than...