Exploring AI algorithms through ant simulations

Project Team

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Nadir Khan (Digital Associate)
Eugene Yu-Dong Zhang (Digital Innovator)
Richard Craggs (Digital Advocate)

Department: Informatics



The project aims to help students engage with and understand Computational Intelligence and software programming. To do this Eugene has developed a simulation of an algorithm that models ants following pheromone trails.

How it works

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Using Matlab, Eugene has created the simulation shown below. The ants start from their ant hill and travel to the food source, laying down pheromones on their trails – shown in blue. Shorter distances is linked to stronger pheromone build up, which in turn causes more ants to follow that trail, leading to more pheromone build up. The end of the video shows almost all the ants using the shortest trail. This simulation, and the interaction of its variables, will be used in a lecture to explain how a computational intelligence algorithm might work.

Abstract: We would like to make students actively involved and engaged in the artificial intelligence (AI), and build help the students to build confidence. The students will learn by doing simulation experiment and gaining experience. They will run open-source codes in group, and learn through collaboration. At least 50 students (CO3091) will benefit from our project. They will participate as a team and practically apply AI knowledge. We aim for students to feel more engaged and motivated, and enjoy learning. Our approach will draw on role-play, reflection, problem-based learning, simulation, and phased learning/mastery methods in teaching, using Matlab and Java as supportive digital tools. We shall divide a big task into small sub-tasks, so the students can learn how to work together. The deadline of this project will be the end of the second semester. We have developed some prototype programs. We need an education consultant, the use of a digital lab and a web-page so we can release our software sustainable. Finally, we can evaluate our project by comparing students’ understanding before and after, along with gathering anonymous feedback on students’ perceptions of the project.


Implementation in Semester 2 2019