FMLbased Machine Learning Tool for Human Emotional Agent with BCI on Music Application This paper presents a human emotional agent based on a fuzzy markup language (FML) tool and machine learning mechanism for music application. The human emotional agent contains a braincomputeinterface (BCI) preprocessing mechanism, a knowledge base, a rule base, a machine learning mechanism, and robots, for music application. We first select some famous songs with various languages, including English, Japanese, Chinese, and Taiwanese, and invite some learners to wear a wireless BCI device to listen to the chosen songs under the designed experimental modes and models. After listening, the learners help label their realtime feelings of each song. We analyze the learnersxe2x80x99 physiological indices transformed from the collected brainwaves to observe whether the learners could have mind communication with the robot. The experimental results show that the FMLbased machine learning tool can help the proposed human emotional agent effectively on the music application. They also show that there is a relationship between the physiological indices and the learnersxe2x80x99 culture, familiarity, and preference for the listened songs.

FMLbased Intelligent Agent for Robotic eLearning and Entertainment Application This paper proposes a fuzzy markup language (FML)based intelligent agent for robotic elearning and entertainment application. We develop a robotic partner system with a robot teacher iPhonoid to interact with students in the classroom to collect studentsxe2x80x99 feedback on the developed iOS app. In this way, the human teachers can make sure that students in their classes whether understand the lesson or not. In addition, we future embed the developed feedback app function to the novel FMLbased intelligent agent with the robot Zenbo for music listening application and edutainment. The developed agent converts human brainwaves into physiological indices and infers subjectsxe2x80x99 feeling while they are listening to music based on the constructed knowledge base (KB) and rule base (RB) of FML. Then the human sends realtime feedback to the FMLbased intelligent agent. Furthermore, the genetic algorithm is utilized for personalized learning the KB and RB of FML tool. The experimental results show that the proposed approach is feasible for elearning and music application. In the future, we will introduce the robot partner system into the teaching field to have more interaction with children to increase their learning simulations.