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1
- Intro
2
- Multiplayer Games
3
- Two Player Game AI
4
- Look-Ahead and Evaluate
5
- Game Tree Size
6
- Look-Ahead as Far as Possible
7
- Two Player Game Tree Min + Max
8
- MaxValue Algorithm single depth
9
- MaxValue Algorithm full tree
10
- MinValue Algorithm
11
- Depth Limit
12
- MiniMax Algorithm
13
- NegaMax Algorithm
14
- MiniMax Properties
15
- Alpha-Beta Pruning
16
- Alpha-Beta Example
17
- Computational Savings
18
- Alpha-Beta Algorithm
19
- Shortening the Algorithm
20
- Recording the Best Action
21
- Time Limit
22
- Iterative Deepening Alpha-Beta
23
- Exam Questions
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it 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

Dave Churchill
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