How do Chess Engines Work - Looking at Stockfish and AlphaZero

Overview

Delve into the fascinating world of chess artificial intelligence with this insightful exploration. Track the groundbreaking advancements from Deep Blue's foundational algorithms to the highly efficient Stockfish and its use of the Minimax strategy.

Experience the next frontier of AI with AlphaZero, which employs cutting-edge self-learning techniques through Monte Carlo Tree Search and neural networks.

This session, available on YouTube, falls under the categories of Artificial Intelligence Courses and Conference Talks, providing a comprehensive look into how these chess programs function and evolve.

Syllabus

• Introduction to Chess AI
Historical overview of chess engines
Importance and impact of AI in chess
• Deep Blue: The Foundation
Overview of Deep Blue's architecture
Rule-based systems and brute force search
Case study: Garry Kasparov vs. Deep Blue
• Stockfish and the Minimax Algorithm
Fundamentals of the minimax algorithm
Role of alpha-beta pruning
Heuristic evaluation functions
Strengths and limitations of Stockfish
Case study: Deep Dive into a Stockfish game
• Introduction to Machine Learning in Chess
Basics of machine learning and neural networks
Transition from rule-based to learning-based systems
• AlphaZero: A New Era
Overview of AlphaZero's architecture
Monte Carlo Tree Search explained
Reinforcement learning and self-play methods
Neural network training processes
Innovations and contributions to AI
• Comparing Stockfish and AlphaZero
The difference in approaches and architectures
Strengths, weaknesses, and practical applications
Performance analysis and comparative results
• Future Trends in Chess AI
Current research directions
Potential advancements and challenges
Ethical considerations and impact on human chess
• Conclusion
Summary of key insights
Future of AI in strategic games
• Course Wrap-up and Final Q&A Session

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Subjects

Conference Talks