Parallel Monte Carlo Tree Search 1st edition by Guillaume Chaslot, Mark Winands, Jaap van den Herik ISBN 3540876076 9783540876076

Parallel Monte-Carlo Tree Search 1st edition by Guillaume M. J. -B. Chaslot, Mark H. M. Winands, H. Jaap van den Herik – Ebook PDF Instant Download/Delivery. 3540876076, 978-3540876076
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Product details:

ISBN 10:       3540876076
ISBN 13: 978-3540876076
Author:         Guillaume M. J. -B. Chaslot, Mark H. M. Winands, H. Jaap van den Herik

Monte-Carlo Tree Search (MCTS) is a new best-first search method that started a revolution in the field of Computer Go. Parallelizing MCTS is an important way to increase the strength of any Go program. In this article, we discuss three parallelization methods for MCTS: leaf parallelization, root parallelization, and tree parallelization. To be effective tree parallelization requires two techniques: adequately handling of (1) local mutexes and (2) virtual loss. Experiments in 13×13 Go reveal that in the program Mango root parallelization may lead to the best results for a specific time setting and specific program parameters. However, as soon as the selection mechanism is able to handle more adequately the balance of exploitation and exploration, tree parallelization should have attention too and could become a second choice for parallelizing MCTS. Preliminary experiments on the smaller 9×9 board provide promising prospects for tree parallelization.

Parallel Monte-Carlo Tree Search 1st  Table of contents:

Chapter 1: Introduction
1.1 Overview of Monte-Carlo Tree Search (MCTS)
1.2 Importance of Parallelism in MCTS
1.3 Challenges in Scaling MCTS for Complex Problems
1.4 Objectives of the Paper
1.5 Contributions of the Paper
1.6 Structure of the Paper

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Chapter 2: Background and Related Work
2.1 Monte-Carlo Tree Search: Principles and Algorithms
2.2 Exploration and Exploitation in MCTS
2.3 Previous Work on Parallel MCTS
2.4 Applications of MCTS in AI and Game Theory
2.5 Challenges in Parallelizing MCTS
2.6 Performance Metrics for Parallel Algorithms

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Chapter 3: Parallelization Techniques for MCTS
3.1 Types of Parallelism in MCTS
3.2 Shared Memory Parallelization
3.3 Distributed Memory Parallelization
3.4 Hybrid Parallelization Approaches
3.5 Load Balancing and Synchronization in Parallel MCTS
3.6 Communication Overhead and Scalability Issues

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Chapter 4: Parallel MCTS Algorithms
4.1 Basic Parallel MCTS Algorithm Framework
4.2 Asynchronous Parallel MCTS
4.3 Synchronous Parallel MCTS
4.4 Parallel UCT (Upper Confidence Bound applied to Trees) Algorithm
4.5 Multi-Threading and GPU Acceleration in MCTS
4.6 Improving Convergence and Exploration in Parallel MCTS

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Chapter 5: Performance Analysis and Complexity
5.1 Time and Space Complexity of Parallel MCTS
5.2 Scalability of Parallel MCTS Algorithms
5.3 Comparative Performance Evaluation: Sequential vs. Parallel MCTS
5.4 Effects of Parallelism on Solution Quality
5.5 Benchmarking Parallel MCTS in Real-World Applications

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Chapter 6: Applications of Parallel MCTS
6.1 Applications in Game AI (e.g., Go, Chess, Shogi)
6.2 Robotics and Path Planning
6.3 Multiplayer and Multi-Agent Systems
6.4 Search and Optimization Problems
6.5 Applications in Simulation and Predictive Modeling

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Chapter 7: Case Studies and Experimental Results
7.1 Parallel MCTS in the Game of Go
7.2 Performance of Parallel MCTS in Chess and Shogi
7.3 Application to Real-Time Decision Making in Robotics
7.4 Experimental Setup and Configuration
7.5 Results and Analysis of Parallel MCTS in Different Domains

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Chapter 8: Challenges and Open Problems
8.1 Scalability in Parallel MCTS for Large State Spaces
8.2 Balancing Exploration and Exploitation in Parallel Environments
8.3 Distributed Systems and Communication Bottlenecks
8.4 Handling Dynamic and Stochastic Environments
8.5 Open Problems and Future Research Directions

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Chapter 9: Conclusion
9.1 Summary of Key Findings
9.2 Impact of Parallelization on MCTS Performance
9.3 Limitations and Potential Improvements
9.4 Future Directions in Parallel MCTS Research
9.5 Final Thoughts on Parallelizing MCTS for Complex Decision-Making Problems

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