Explore a comprehensive lecture on artificial intelligence game-playing algorithms, focusing on Minimax, Alpha-Beta pruning, and their implementation. Dive into multiplayer game strategies, tree search techniques, and algorithm optimizations. Learn about look-ahead evaluation, game tree size considerations, and iterative deepening. Gain practical insights through exam question discussions and a detailed explanation of Assignment 3, including GUI usage, testing functions, and code implementation for various player types. Master the intricacies of AlphaBeta debugging and understand the marking scheme for the assignment.
Introduction to Artificial Intelligence: Minimax, Alpha-Beta Pruning, and Assignment 3 - Lecture 10
Overview
Syllabus
- Preroll
- Greetings
- Lecture Start
- Multiplayer Games
- Game Playing Algorithms
- Look-Ahead and Evaluate
- Game Tree Size
- Look-Ahead as Far as Possible
- Tree Search with Multiple Players
- MaxValue and MinValue Functions
- MiniMax Algorithm
- Megamax Algorithm
- MiniMax Properties & NE
- Alpha-Beta Pruning
- Alpha-Beta Visual Example
- Alpha-Beta Computational Savings
- Alpha-Beta MaxValue and MinValue
- Alpha-Beta Single Algorithm
- Shortening the Alpha-Beta Algorithm
- Recording the Best Action
- Implementing a Time Limit
- Iterative Deepening Alpha-Beta
- Exam Questions
- Assignment 3 Explained
- A3 GUI Explained
- A3 Testing Functions
- A3 Code - GameState
- Sample Players
- Player_Student
- IDAlpheBeta Function
- AlphaBeta Function
- Eval Function
- Marking Scheme
- AlphaBeta Debugging Tips
Taught by
Dave Churchill
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