A Collaborative Learning System Based on Learners' Individuality Using Multi-agent

In order to solve the problems existed in the present remote learning system, such as the monotonous pattern and passive educating, a multi-agent learning system model based on learner's individuality is presented in this paper. Integrate wit intelligent agent technology, the multi-agent learning system gives an agent capability description language by analyzing learner's personality, on the basis of which we suggest relevant personal grouping strategy and learning task allocation strategy. Furthermore, it uses compensates mechanism to encourage the agent cooperation, state-space-search theory to enable the MAS system to have the stronger problem solving ability, both of which can meet demand of active learning for the learners and to some extent economize communication of system and save the communication in the system in certain degree.