Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

Abstract

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we infer students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork spirit in the class. Moreover, we combine the optimization techniques such as genetic algorithm (GA) and particle swarm optimization (PSO) with FML, called GFML and PFML, respectively, to learn the constructed knowledge base and rule base. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.