PSO-Based CI Agent with Learning Tool for Student Experience in Real-World Application

This paper proposes a particle swarm optimization (PSO)-based computational intelligence (CI) agent with learning tools for young students’ experience and learning CI application. The CI agent combines human languages that inspire fuzzy systems with machine languages based on PSO. CI, including fuzzy systems (FSs), evolutionary computation (EC), and neural networks (NNs), is an imperative branch of artificial intelligence (AI). As a core technology of AI, it plays a vital role in developing intelligent systems and agents. During the CI Sandbox Workshop and Competition at IEEE CEC 2023 in the USA and FUZZ-IEEE 2023 in Korea, we organized a workshop with an associated competition for young students to learn and experience CI using the CI&AI-FML learning tool with a PSO-based CI agent. First, young students receive learning materials and guidance from tutors to learn about CI-related basic concepts. Then, they are enabled to test the learned materials with the CI learning tool in a simple real-world application. Three experiments, including an advanced driver assistance system (ADAS) at IEEE CEC 2023, an intelligent agriculture system (IAS) as well as a smart greenhouse system (SGS) at FUZZ-IEEE 2023 were conducted in these two CI sandboxes to real-time collect data from the sensors of the CI&AI-FML learning tool and send them to the PSO-based CI agent to make inferences. Finally, the learning tool activates according to the inferred result of the CI agent for young students’ experience in real-world applications. In the future, we will extend the PSO-based CI agent with Quantum Computational Intelligence (QCI) to more countries for young students co-learning CI with smart machines.