In this paper, we propose an AI-FML robotic agent for student learning behavior ontology construction which can be applied in English speaking and listening domain. The AI-FML robotic agent with the ontology contains the perception intelligence, computational intelligence, and cognition intelligence for analyzing student learning behavior. In addition, there are three intelligent agents, including a perception agent, a computational agent, and a cognition agent in the AI-FML robotic agent. We deploy the perception agent and the cognition agent on the robot Kebbi Air. Moreover, the computational agent with the Deep Neural Network (DNN) model is performed in the cloud and can communicate with the perception agent and cognition agent via the Internet. The proposed AI-FML robotic agent is applied in Taiwan and tested in Japan. The experimental results show that the agents can be utilized in the human and machine co-learning model for the future education.
- AI-FML ,
- Agent ,
- Robot ,
- Fuzzy Machine Learning ,
- Ontology ,
- Learning Behavior
- Robots ,
- Cognition ,
- Artificial intelligence ,
- Ontologies ,
- Speech recognition ,
- Education ,
- Neural networks
What is the relationship between Artificial Intelligence (AI) and Computational Intelligence (CI)? According to IEEE Computational Intelligence Society (CIS), CI is the theory, design, application and development of biologically and linguistically motivated computational paradigms . It plays an important role in developing successful intelligent systems, including games, multilayer perceptron, and cognitive developmental systems, for example, Deep Learning (DL) or Deep Convolutional Neural Networks (DCNN). DL or DCNN is one of the core methods for AI systems . In addition, using the human language as a source of inspiration, fuzzy logic systems (FLS) can model linguistic imprecision and solve uncertain problems . FLS enables us to perform approximate reasoning based on humanized thinking, and it includes fuzzy sets and fuzzy logic, fuzzy clustering and classification, linguistic summarization, fuzzy neural networks, type-2 fuzzy sets and systems, and so on . B. Bouchon-Meunier, President of the IEEE CIS in 2020-2021, wrote her welcome message in the IEEE CIS Website to the readers as follows : “…One century ago, in 1920, the word “robot” appeared, … 1920 is also the year when the writer Isaac Asimov was born and everyone knows his Three Laws of Robotics, which he first published in 1942 in the novel Runaround. This novel was the first in a long series of stories in Fig. 1 shows the system structure for student learning performance assessment with brain computer interface (BCI) mechanism which describes as follows : The subject wearing a BCI device interacts with the intelligent learning assessment robot and AI-FML robot to learn English through listening or speaking . Meanwhile, the data related to his/her expressions, emotions, and learning performance are collected to store in the cloud resources/database. The left side of the brain is better at things like reading, writing, and computations . However, the right brain has a more creative and less organized way of thinking . Hence, the robot can help humans to do things in a more rational thinking, and human can add much creative information and knowledge to robotic behavior. Therefore, humans can work with the robot in the world such as listening to music or Go applications for human and machine co-learning in future education [7-10]. In this paper, we propose an AI-FML robotic agent for student behavior ontology construction and analyze student learning behavior. Kebbi Air robots with AI- FML robotic agent were deployed in Taiwan and tested in Japan. The research question in this paper is try to observe student learning behavior in class to construct their learning behavior ontology. Fuzzy markup language (FML) is a human machine language and it is able to establish the communication bridge between humans and machines. Humans interact with the robots to make learning fun and the robots collect the information from humans to make themselves much smarter after machine learning. That’s way, we use FML as a communication language between humans and machines. The experimental results show the agents can be utilized in the human and machine co-learning model for the future education. The remainder of the paper is organized as follows. We first introduce the system structure of the AI-FML robotic agent in Section II. We then analyze and construct the student learning behavior extraction mechanism based on the robotic agent in Section III. Section IV presents the exploration and exploitation for learning behavior ontology construction. Experimental results are given in Section V and finally, we conclude the paper in Section VI.
This paper proposes an AI-FML robotic agent for student learning behavior ontology construction, including the perception agent, cognition agent, and computational agent, for analyzing student learning behavior. Moreover, the perception agent and the cognition agent are deployed in the Kebbi Air robot. In addition, the computational agent with the DNN model can communicate with the perception agent and cognition agent via the Internet. The proposed AI-FML robotic agent has been applied to the teaching fields in Taiwan and tested in Japan. The involved students interacted with the Kebbi Air to co-learn English and AI-FML in class which makes learning fun for students. The experimental results show that the agents can be utilized in the human and machine co-learning model for future education.
The authors would like to thank the staff of the Center for Research of Knowledge Application & Web Service (KWS Center) of NUTN and the involved faculty and students of Rende elementary school, Guiren elementary school, and Rende junior high school in Taiwan, and the members of Yamaguchi Lab. and Kubota Lab. of TMU in Japan.
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.
Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.
The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.
FULL Paper PDF file:A Study on AI-FML Robotic Agent for Student Learning Behavior Ontology Construction
C. -S. Lee et al.,
A Study on AI-FML Robotic Agent for Student Learning Behavior Ontology Construction
2020 International Symposium on Community-centric Systems (CcS), Hachioji, Tokyo, Japan, 2020, pp. 1-6,
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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.