Intelligent agent for real-world applications on robotic edutainment and humanized co-learning

Lee, Chang-Shing; Wang, Mei Hui; Tsai, Yi Lin; Ko, Li-Wei; Tsai, Bo Yu; Hung, Pi Hsia; Lin, Lu An; Kubota, Naoyuki

doi:10.1007/s12652-019-01454-4

Cited by 23 publications

(9 citation statements)

References 11 publications

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“…The wearable BCI system benefits machine-human co-learning as it can be integrated with training methods or neurofeedback to monitor and regulate Go players’ physiological states, helping them to maintain their attention and overcome the competitive pressure experienced in the game. Later, the system was extended to the educational domain interfacing with an intelligent agent for robotic edutainment in 2019 ( Lee et al, 2019 ; Figure 9B ). The experimental results showed that through playing games, the system improved the interest and performance of students in mathematical and language learning.…”

Section: Discussionmentioning

confidence: 99%

Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings

Chang

Tsai

et al. 2021

Front. Hum. Neurosci.

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Mental state changes induced by stimuli under experimental settings or by daily events in real life affect task performance and are entwined with physical and mental health. In this study, we developed a physiological state indicator with five parameters that reflect the subject’s real-time physiological states based on online EEG signal processing. These five parameters are attention, fatigue, stress, and the brain activity shifts of the left and right hemispheres. We designed a target detection experiment modified by a cognitive attention network test for validating the effectiveness of the proposed indicator, as such conditions would better approximate a real chaotic environment. Results demonstrated that attention levels while performing the target detection task were significantly higher than during rest periods, but also exhibited a decay over time. In contrast, the fatigue level increased gradually and plateaued by the third rest period. Similar to attention levels, the stress level decreased as the experiment proceeded. These parameters are therefore shown to be highly correlated to different stages of the experiment, suggesting their usage as primary factors in passive brain-computer interfaces (BCI). In addition, the left and right brain activity indexes reveal the EEG neural modulations of the corresponding hemispheres, which set a feasible reference of activation for an active BCI control system, such as one executing motor imagery tasks. The proposed indicator is applicable to potential passive and active BCI applications for monitoring the subject’s physiological state change in real-time, along with providing a means of evaluating the associated signal quality to enhance the BCI performance.

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Section: Discussionmentioning

confidence: 99%

Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings

Chang

Tsai

et al. 2021

Front. Hum. Neurosci.

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“…(Nikolaidis et al, 2017b;Mohammad and Nishida., 2008;Nikolaidis et al, 2017a)]. The studies that use "co-learning" tend to take a more symmetrical approach by looking at agent or robot learning as well as human learning, and pay more attention to the learning process and changing strategies of the human as well, often looking at many repetitions of a task (Ramakrishnan, Zhang, and Shah 2017;C.-S. Lee et al, 2020;C. Lee et al, 2018;Shafti et al, 2020).…”

Section: Co-learning: Background and Definitionmentioning

confidence: 99%

“…( Nikolaidis et al, 2017b ; Mohammad and Nishida., 2008 ; Nikolaidis et al, 2017a )]. The studies that use “co-learning” tend to take a more symmetrical approach by looking at agent or robot learning as well as human learning, and pay more attention to the learning process and changing strategies of the human as well, often looking at many repetitions of a task ( Ramakrishnan, Zhang, and Shah 2017 ; C.-S. Lee et al, 2020 ; C. Lee et al, 2018 ; Shafti et al, 2020 ). Studies on co-evolution, on the other hand, monitor a long-term real-world application in which behavior of the human as well as the robot subtly changes over time ( Döppner, Derckx, and Schoder 2019 ).…”

Section: Co-learning: Background and Definitionmentioning

confidence: 99%

Becoming Team Members: Identifying Interaction Patterns of Mutual Adaptation for Human-Robot Co-Learning

2021

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Becoming a well-functioning team requires continuous collaborative learning by all team members. This is called co-learning, conceptualized in this paper as comprising two alternating iterative stages: partners adapting their behavior to the task and to each other (co-adaptation), and partners sustaining successful behavior through communication. This paper focuses on the first stage in human-robot teams, aiming at a method for the identification of recurring behaviors that indicate co-learning. Studying this requires a task context that allows for behavioral adaptation to emerge from the interactions between human and robot. We address the requirements for conducting research into co-adaptation by a human-robot team, and designed a simplified computer simulation of an urban search and rescue task accordingly. A human participant and a virtual robot were instructed to discover how to collaboratively free victims from the rubbles of an earthquake. The virtual robot was designed to be able to real-time learn which actions best contributed to good team performance. The interactions between human participants and robots were recorded. The observations revealed patterns of interaction used by human and robot in order to adapt their behavior to the task and to one another. Results therefore show that our task environment enables us to study co-learning, and suggest that more participant adaptation improved robot learning and thus team level learning. The identified interaction patterns can emerge in similar task contexts, forming a first description and analysis method for co-learning. Moreover, the identification of interaction patterns support awareness among team members, providing the foundation for human-robot communication about the co-adaptation (i.e., the second stage of co-learning). Future research will focus on these human-robot communication processes for co-learning.

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“…Fig. 1 shows the system structure for student learning performance assessment with brain computer interface (BCI) mechanism which describes as follows [7]: The subject wearing a BCI device interacts with the intelligent learning assessment robot and AI-FML robot to learn English through listening or speaking [7]. Meanwhile, the data related to his/her expressions, emotions, and learning performance are collected to store in the cloud resources/database.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the robot can help humans to do things in a more rational thinking, and human can add much creative information and knowledge to the 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][8][9][10]. In this paper, we propose an AI-FML robotic agent for student behavior ontology construction and analyze student learning behavior.…”

Section: Introductionmentioning

confidence: 99%

A Study on AI-FML Robotic Agent for Student Learning Behavior Ontology Construction

Lee

Wang

Kuan

et al. 2020

2020 International Symposium on Community-Centric Systems (CcS)

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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.

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Intelligent agent for real-world applications on robotic edutainment and humanized co-learning

Cited by 23 publications

References 11 publications

Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings

Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings

Becoming Team Members: Identifying Interaction Patterns of Mutual Adaptation for Human-Robot Co-Learning

A Study on AI-FML Robotic Agent for Student Learning Behavior Ontology Construction

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