Computers in Chess Solving Inexact Search Problems 1st Edition by Botvinnik, Brown, Reznitsky, Stilman, Isfasman, Yudin ISBN 1461297362 9781461297369

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Product details:
ISBN 10: 1461297362
ISBN 13: 9781461297369
Author: Botvinnik, Brown, Reznitsky, Stilman, Isfasman, Yudin

Computers in Chess Solving Inexact Search Problems 1st Edition Table of contents:

Part I: Foundations of Chess Computation

2. The Mechanics of Chess and the Challenge of Search

   • Understanding the Game of Chess: Rules and Strategies
   • The Role of Search in Chess Algorithms
   • Challenges of Inexact Search: Computational Complexity in Chess

3. Historical Development of Chess Algorithms

   • Early Computer Chess: From Turing to Modern Day
   • The Evolution of Search Methods in Chess Programs
   • Milestones in Chess AI: Deep Blue and Beyond

Part II: Inexact Search Problems in Chess

4. Search Algorithms and Evaluation Functions

   • Classic Search Algorithms: Minimax, Alpha-Beta Pruning
   • Handling Inexact Search through Heuristics
   • Designing Effective Evaluation Functions for Chess Positions
   • Case Study: Balancing Search Depth and Evaluation Accuracy

5. Dealing with Uncertainty in Chess Computation

   • Probabilistic Approaches to Chess Search
   • Managing Inexact Knowledge of Chess Positions
   • Using Monte Carlo Methods for Inexact Search Problems
   • Case Study: Inexact Search in Chess Endgames

6. Reducing Search Space in Chess Solvers

   • Pruning Techniques to Improve Efficiency
   • Selective Search and Its Impact on Performance
   • Alpha-Beta Pruning and Extensions for Faster Computation
   • Case Study: Search Space Reduction in Top Chess Engines

Part III: Advanced Techniques in Chess Search

7. Machine Learning and Neural Networks in Chess

   • The Role of Machine Learning in Improving Chess Play
   • Neural Networks for Pattern Recognition in Chess
   • Reinforcement Learning in Chess Strategy Development
   • Case Study: Using Deep Learning for Chess Move Prediction

8. Parallel and Distributed Processing in Chess Computation

   • The Benefits of Parallelism in Search Algorithms
   • Distributed Systems for Handling Large Search Spaces
   • Case Study: Parallelization in Modern Chess Engines
   • Limitations and Opportunities in Distributed Chess Computation

9. Approximate Search Methods for Chess

   • Understanding Approximation and Its Benefits in Chess AI
   • Alpha-Beta Approximation and Other Search Techniques
   • Approximate Search in Real-Time Chess Applications
   • Case Study: Real-Time Chess Engines and Inexact Search

Part IV: Practical Applications and Future Directions

10. Chess Engines and Inexact Search: Practical Applications

    • Implementing Inexact Search in Commercial Chess Programs
    • Inexact Search in Chess Tournaments and Online Play
    • Case Study: The Role of Search in Chess Championships
    • The Impact of Search Algorithms on Chess AI Ratings

11. Future Directions in Chess Search Algorithms

    • Emerging Trends in Chess AI and Search Technology
    • Quantum Computing and Its Potential in Chess
    • The Next Frontier: Solving Complex Chess Problems with AI
    • Case Study: The Future of Chess AI and Human-AI Interaction

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