Explore a comprehensive lecture on artificial intelligence techniques for two-player games, focusing on the MiniMax algorithm and Alpha-Beta pruning. Dive into multiplayer game AI concepts, game tree analysis, and look-ahead strategies. Learn about MaxValue and MinValue algorithms, depth limits, and the NegaMax variation. Understand the properties of MiniMax and the computational savings achieved through Alpha-Beta pruning. Discover how to implement time limits and iterative deepening in Alpha-Beta search. Gain practical insights into algorithm optimization and best action recording for AI decision-making in game environments.
Introduction to Artificial Intelligence: MiniMax and AlphaBeta Search - Lecture 10
Overview
Syllabus
- Intro
- Multiplayer Games
- Two Player Game AI
- Look-Ahead and Evaluate
- Game Tree Size
- Look-Ahead as Far as Possible
- Two Player Game Tree Min + Max
- MaxValue Algorithm single depth
- MaxValue Algorithm full tree
- MinValue Algorithm
- Depth Limit
- MiniMax Algorithm
- NegaMax Algorithm
- MiniMax Properties
- Alpha-Beta Pruning
- Alpha-Beta Example
- Computational Savings
- Alpha-Beta Algorithm
- Shortening the Algorithm
- Recording the Best Action
- Time Limit
- Iterative Deepening Alpha-Beta
- Exam Questions
Taught by
Dave Churchill
