Explore the intersection of artificial intelligence and gaming by diving into the algorithms that power strategic decision-making in classic games. In this hands-on Python course, students will learn core AI gaming algorithms such as Minimax, Alpha-beta Pruning, and Monte Carlo Tree Search (MCTS) and apply them to games like Tic-Tac-Toe, Connect 4, and beyond.
The course will culminate in a final project, where students will design an original game concept (that will fit within the scope and capacity of this course), implementing one or more AI algorithms to create a competitive or challenging game experience. By the end of the course, students will understand how AI strategies can enhance gameplay and develop their own AI-driven game.
Anticipated Credit Equivalencies:
8 - Artificial Intelligence (Upper Division)
Registration
Academic Details
Computer Science, Software Engineering, Artificial Intelligence
Computer Science