An Experience-Type Car Maintenance Training System based on Logic Simulation

Recently, researches on the application of IT technology to various fields including traditional industries are becoming more popular. One challenge in the field of education is to understand the way how technology may support learning, and research on self-directed learning has been accelerated by integrating education and IT technology. The process of self-directed learning in e-learning applications such as Car Maintenance Training is very difficult and complicated. Previous studies on car maintenance training applications provided simple training scenarios with predetermined action sequences. To incorporate self-directed learning in car maintenance training, however, trainees must be able to perform various maintenance operations himself and experience various situations. To provide such functionality, it is necessary to obtain an accurate response for various operations of trainees, but it requires complicated calculations with respect to varieties in the electrical and mechanical processes of a car. In this paper, we develop a logic simulation agent using JESS inference engine in which self-directed learning is achieved by capturing the behavior of trainees and simulating car operations without complicated physical simulations in car maintenance training.