Главная
Study mode:
on
1
- Intro
2
- Lecture Start
3
- Useful Links
4
- Actions / Moves
5
- State Evaluation
6
- Types of Enhancements
7
- Incremental Progress
8
- Tie-Breaking Scores
9
- State Depth Parity Effect
10
- Avoiding State Copies
11
- Move Ordering
12
- Search Extensions
13
- Bit Operations
14
- Bit Sets
15
- XOR Operator
16
- Bit Boards
17
- Transposition Tables
18
- Zobrist Hashing
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore advanced techniques for enhancing minimax search algorithms in this comprehensive lecture on artificial intelligence. Delve into crucial concepts such as actions and moves, state evaluation, and various types of enhancements. Learn about incremental progress, tie-breaking scores, and the state depth parity effect. Discover strategies for avoiding state copies and implementing effective move ordering. Investigate search extensions and bit operations, including bit sets and the XOR operator. Examine the use of bit boards for efficient game state representation. Finally, gain insights into transposition tables and Zobrist hashing for improved search performance. This lecture, part of a graduate-level AI course, provides essential knowledge for developing sophisticated game-playing algorithms.

Minimax Search Enhancements in Artificial Intelligence - Lecture 11

Dave Churchill
Add to list
0:00 / 0:00