Knowledge Inferencing on Chinese Chess Endgames 1st edition by Bo Nian Chen, Pangfeng Liu, Shun Chin Hsu, Tsan sheng Hsu ISBN 3540876076 9783540876076

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Product details:

ISBN 10:       3540876076
ISBN 13: 978-3540876076
Author:         Bo Nian Chen, Pangfeng Liu, Shun-Chin Hsu, Tsan-sheng Hsu

Several Chinese chess programs exhibit grandmaster playing skills in the opening and middle game. However, in the endgame phase, the programs only apply ordinal search algorithms; hence, they usually cannot exchange pieces correctly. Some researchers use retrograde algorithms to solve endgames with a limited number of attack pieces, but this approach is often not practical in a real tournament. In a grandmaster game, the players typically perform a sequence of material exchanges between the middle game and the endgame, so computer programs can be useful. However, there are about 185 million possible combinations of material in Chinese chess, and many hard endgames are inconclusive even to human masters. To resolve this problem, we propose a novel strategy that applies a knowledge-inferencing algorithm on a sufficiently small database to determine whether endgames with a certain combination of material are advantageous to a player. Our experimental results show that the performance of the algorithm is good and reliable. Therefore, building a large knowledge database of material combinations is recommended.

Knowledge Inferencing on Chinese Chess Endgames 1st Table of contents:

Chapter 1: Introduction
1.1 Overview of Chinese Chess (Xiangqi)
1.2 Importance of Endgames in Chinese Chess
1.3 Knowledge Inferencing and Its Role in Game Strategy
1.4 Objectives and Contributions of the Paper
1.5 Structure of the Paper

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Chapter 2: Background and Related Work
2.1 Fundamentals of Chinese Chess (Xiangqi)
2.2 Endgame Strategy in Chinese Chess
2.3 Artificial Intelligence in Chess and Xiangqi
2.4 Knowledge Representation and Inferencing in Game Theory
2.5 Related Work on Chinese Chess Endgames
2.6 Challenges in Chinese Chess Endgame Analysis

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Chapter 3: Knowledge Inferencing Framework
3.1 Principles of Knowledge Inferencing
3.2 Formal Representation of Chinese Chess Endgames
3.3 Inference Models in Game Theory
3.4 Knowledge Base Construction for Xiangqi Endgames
3.5 Inferencing Algorithms for Endgame Positions
3.6 Combining Rule-Based and Statistical Approaches

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Chapter 4: Modeling Chinese Chess Endgames
4.1 Endgame Scenarios in Chinese Chess
4.2 Key Endgame Positions and Patterns
4.3 Constructing a Knowledge Database for Xiangqi Endgames
4.4 Endgame Reduction Techniques
4.5 Use of Heuristics in Endgame Evaluation
4.6 Transitioning from Opening to Endgame with Knowledge-Based Inference

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Chapter 5: Algorithms for Knowledge Inferencing
5.1 Inferencing Techniques for Chinese Chess Endgames
5.2 Minimax and Alpha-Beta Pruning in Xiangqi Endgames
5.3 Monte Carlo Tree Search (MCTS) for Endgame Scenarios
5.4 Rule-Based Systems for Endgame Decision Making
5.5 Learning Endgame Strategies through Knowledge Inferencing
5.6 Hybrid Approaches: Combining Knowledge Inferencing with Machine Learning

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Chapter 6: Performance Evaluation and Case Studies
6.1 Evaluation Metrics for Endgame Knowledge Inferencing
6.2 Case Study 1: Endgame Position Evaluation and Inference
6.3 Case Study 2: Knowledge Inferencing in Complex Endgame Scenarios
6.4 Comparison with Traditional Search-Based Algorithms
6.5 Analysis of Knowledge Inferencing Efficiency
6.6 Lessons Learned from Case Studies

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Chapter 7: Applications of Knowledge Inferencing in Chinese Chess
7.1 Optimizing Endgame Strategies for AI Players
7.2 Real-Time Knowledge Inferencing for Chinese Chess Engines
7.3 Endgame Training Tools for Human Players
7.4 Applications in Other Strategy Games and Board Games
7.5 Integration with Chinese Chess Online Platforms

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Chapter 8: Challenges and Future Directions
8.1 Complexity in Modeling and Inferencing for Chinese Chess Endgames
8.2 Scalability Issues in Knowledge Representation
8.3 Handling Ambiguities and Uncertainty in Endgame Positions
8.4 Integration of Deep Learning and Knowledge Inferencing
8.5 Future Directions in Game AI and Knowledge-Based Inference for Xiangqi

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Chapter 9: Conclusion
9.1 Summary of Key Findings and Contributions
9.2 Impact of Knowledge Inferencing on Chinese Chess Endgames
9.3 Limitations of the Current Approach
9.4 Final Thoughts and Outlook for Future Research

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