Improved Agent-Based Learning Platform for Introductory AI

We developed a web-based application to improve a recently published agent-based learning platform for introductory artificial intelligence. Our application is an interactive tool for learning introductory artificial intelligence principles. Specifically, this work considers AI-based classical search algorithms. Our interactive tool allows a user to select a search type (BFS, DFS, A*, etc.), the number of agents (single or multi-agent), and the size of the environment to run tests on. It further delivers a summary of performance diagnostics gathered from experiments. We provide a discussion of the design, implementation, and results gathered from experiments using our interactive tool.