This pathfinding project was part of my artificial intelligence course at Full Sail University. Several AI concepts were discussed and practiced in this class, including breadth-first search; depth-first search; in-order, pre-order, and post-order traversal; behavior trees; state machines; best-first search; greedy search; uniform-cost search; A* search; path planning; boids; minimax searching; expectminimax searching; alpha-beta pruning; and neural networks.

 

I used this project for a majority of the class to practice and understand how pathfinding works using different types of tree searching. I began by using breadth-first search, which is extremely inefficient. Once I understood this, I moved onto the best-first search algorithm. After this, I implemented greedy search, then uniform-cost search, and then A*. Currently, A* is the most efficient known pathfinding algorithm for artificially intelligent characters in video games. This pathfinding project has served as an excellent visual and practical example for me, and has inspired me to become more involved with artificial intelligence.