This basic implementation of a look-ahead minimax algorithm plays the game Reversi, sometimes known as Othello, with a customizable look ahead depth and some knowledge of the game, such as awarding more points (internally) for edge and corner pieces as well as recognizing an end-game state and avoiding or attempting to reach that state.

This project implements the A* pathfinding algorithm into a hexagonal grid with various test cases that involve different maps and start and end positions. The maps include terrain weight, shown by shades of gray. The algorithm can be slowed down to view it step by step along with a visualization of its progress or be generated as fast as possible.

Note: An EXE download is not available for this project. Please request the source code if interested.

This project implements a simple flocking algorithm with customizable alignment, cohesion, and separation variables. Rotational forces are applied to boids with a constant forward force to navigate them naturally instead of directly manipulating their positions. Multiple boid groups can be spawned at once.

This simple console application demonstrates knowledge of behavior trees in two stages. The first stage traverses a somewhat large behavior tree in bredth-first order, preorder, and postorder. The second stage uses a behavior tree to navigate an agent through a world to collect a gold piece, find and kill a wumpus, and avoid pitfalls.