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Abstract
Computational Intelligence (CI), which includes fuzzy logic (FL), neural network (NN), and evolutionary computation (EC), is an imperative branch of artificial intelligence (AI). As a core technology of AI, it plays a vital role in developing intelligent systems, such as games and game engines, neural-based systems including a variety of deep network models, evolutionary-based optimization methods, and advanced cognitive techniques.
The idea is to create a young student workshop with an associated competition during FUZZ-IEEE 2023 inspired by the F.I.R.S.T. robotics competition. Before or during the first hours of the event, students will receive learning materials and guidance from tutors to learn about a CI-related topic. Later students will be enabled to test learned materials in a simple real-world application. Namely, programming robots to solve various tasks.
Activitiy Goals
Learn more about machine learning and how it shapes our world.
Teach a robot to recognize objects and make recommendations to plan your next journey.
Engage in a fun competition that tests the skills you have learned during the workshop.
Registration Information
Open to all middle/high school and undergraduate students, no prerequisites required.
25$ registration fee per participant covering the costs of the workshop (excluding traveling and lodging).
Activitiy Pattern
A three-hour Workshop.
A three-hour Practice Session.
A Competition.
Machine-Human Co-learning Model
The model is suitable for learning different age-group students and is described as follows.
The starting point is Human Intelligence (HI). Then, it unfolds into CI machine learning models, including perception abilities based on Neural Networks (NN), cognition skills with Fuzzy Logic (FL), and evaluation methods based on Evolutionary Computation (EC) techniques.
During the learning process, the model identifies four stages for young students to gain familiarity with AI-FML: conceptual-based learning, experience-based learning, operation-based learning, practice-based learning, application-based learning, and expression-based learning.
The students learn with the support of machines/robots – co-learning – following the integration of elements of CI with real-life applications and learning how to interact with the machines/robots.
Six-Step Co-Learning Process for Human and Machine
The six-step co-learning procedure for human intelligence and machine learning inspired by the Heart Sutra is described as follows. Humans and machines learn together through a process of
observing and attending the learning environment (Step 1),
studying the detailed knowledge based on the learner's intelligence (Steps 2 and 3),
utilizing what they have learned (Step 4),
understanding specific domain knowledge (Step 5), and finally
explaining it to new learners (Step 6).
This interaction is repeated continuously to achieve the goal of co-learning between humans and machines.
Learning Framework of AIoT based on AI-FML
Learning framework of the AI-FML AIoT is described as follows.
Students utilize the AI-FML learning platform to construct the knowledge base and rule base of the CI-based real-world applications. Additionally, they can apply the Particle Swarm Optimization (PSO) tool to train the knowledge base (KB) and rule base (RB) using the training data set.
Students make use of the AI-FML Lab to program the AI-FML Blockly. They can operate the fuzzy inference mechanism according to the input data and the learned Adaptive Network-Based Fuzzy Inference System (ANFIS) model, or PSO-based Fuzzy Markup Language (PFML)-based KB and RB.
Students adopt the NUWA Lab to interact with the AI-FML robot Kebbi Air. They can co-learn with the robot to simulate the features of the robot’s perception and cognition.
The inferred results are sent to the AI-FML robot Kebbi Air through the Message Queuing Telemetry Transport (MQTT) server. Besides, the AI-FML Lab and AI-FML learning platform can directly send inferred results to the AI-FML robot Kebbi Air or the AIoT-FML MoonCar through the MQTT server and to the AIoT-FML-LT through HTTP protocol.
Finally, the end-user can communicate with the remote end-user online with such a framework.
CI&AI-FML Metaverse scenario with AI-FML Kebbi Air and AI-FML MoonCar
CI&AI-FML Metaverse scenario is described as follows.
The student with a BCI device controls a drone to fly from Taiwan through Tur-key to arrive in Italy which is the place to hold IEEE WCCI 2022.
When he arrives in Italy, he takes an AI-FML MoonCar to start his sightseeing by visiting an AR Museum, joining an AR Creativity, and attending an AR Band.
During the journey, the student can experience and practice the CI&AI-FML learning, including KB&RB construction to control the speed of the AI-FML MoonCar, image recognition technology to make the MoonCar recognize the traffic signs, Blocky design to train the capability of program logic, and body perception interaction with the AI-FML devices including Kebbi Air and MoonCar to carry out the STEAM education.
Team CI Super @ IEEE WCCI 2022
Tsoying Senior High School, Kaohsiung, Taiwan
CI&AI-FML Experience and Learning Model for Pre-University Student Practice
CI&AI-FML Experience and Learning Model for Pre-University Student Practice is described as follows.
Part 1 Machine Learning Experience: Training the machine learning model by Google Teachable Machine, including images recognition, voice recognition, and pose recognition.
Part 2 CI Learning: Apply CI&AI-FML Learning Platform and CI&AI-FML Lab. to simulate the Adaptive-Network-Based Fuzzy Inference System (ANFIS) model, construct the Knowledge Base (KB) with Rule Base (RB), and practice evolution-ary computation (EC) techniques such as Particle Swarm Optimization (PSO) or Genetic Algorithm (GA), and.
Part 3 CI Application Practice: Integrate CI&AI-FML Robot, CI&AI-FML-LT, and CI&AI-FML MoonCar for real-world application with travel recommenda-tion.
Image Data Collection and Machine Learning
AI-FML Robot Setting and the Blockly Program Design
Combine AI-FML Travel Recommendation with Robot Image Recognition
AI-FML Travel Recommendation Application with Image Recognition
Rende Elementary School students demonstrate AI-FML Travel Recommendation with Robot Voice & Image Recognition
AI-FML Travel Recommendation Application with Image Recognition
Registration Instructions
Registration must be received before May 31, 2023 via the competition website (Register).
Registration Deadline
May 31, 2023
Available Software Tools
VisualFMLTool : It can be executed on platforms containing the Java Runtime Environment. The Java Software Development Kit, including JRE, compiler and many other tools can be found at here. The VisualFMLTool can download from here and then to extract it. Then it is only needed to click the file VisualFMLTool.bat included in the zip to execute the tool.
AI-FML Learning Platform : It is developed by KWS center/OASE Lab., NUTN, Taiwan and can be executed on different platforms online. After registering the competition, we can provide an account for the participants.
ZAI-FML Learning Platform : It is developed by Zsystem Co. Ltd., Taiwan and can be executed on different platforms online. The participants can apply for a trial account online.
JFML : A spanish research group (Jose Manuel Soto Hidalgo, Giovanni Acampora, Jesus Alcala Fernandez, Jose Alonso Moral) has released a library for FML programming that is very simple to use and compliant with IEEE 1855. JFML can download from here. Additional information about the library is here.
Some References associated to JFML
J. M. Soto-Hidalgo, Jose M. Alonso, G. Acampora, and J. Alcala-Fdez, "JFML: A Java library to design fuzzy logic systems according to the IEEE Std 1855-2016," IEEE Access, vol. 6, pp. 54952-54964, 2018.
J. M. Soto-Hidalgo, A. Vitiello, J. M. Alonso, G. Acampora, J. Alcala-Fdez, "Design of fuzzy controllers for embedded systems with JFML," International Journal of Computational Intelligence Systems, vol. 12, no. 1, pp. 204-214, 2019.
GTC Showcase App : The GTC Showcase app provides news and demos of our research projects to the public. Augmented Creativity aims to enhance creative and playful activities using augmented reality (AR). This app contains three modules, including the AR coloring book module, AR Band module, and AR Museum module.
F. Zund, M. Ryffel, S. Magnenat, A. Marra, M. Nitti, M. Kapadia, G. Noris, K. Mitchell, M. Gross, and R. W. Sumner, "Augmented creativity: Bridging the real and virtual worlds to enhance creative play," ISIGGRAPH ASIA 2015 Mobile Graphics and Interactive Applications, no. 21, pp. 1-7, 2015.
Reference
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