Summary of Xqsv: a Structurally Variable Network to Imitate Human Play in Xiangqi, by Chenliang Zhou
XQSV: A Structurally Variable Network to Imitate Human Play in Xiangqi
by Chenliang Zhou
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel deep learning architecture called Xiangqi Structurally Variable (XQSV), which emulates the behavioral patterns of human Xiangqi players by dynamically altering its structural configuration. The design incorporates several improvements, including a local illegal move filter, Elo range partitioning, and sequential one-dimensional input. Empirically, XQSV achieves a predictive accuracy of 40%, peaking within the trained Elo range. A three-terminal Turing Test demonstrates that XQSV imitates human behavior more accurately than conventional engines. The authors propose two relaxed evaluation metrics for nondeterministic human gameplay. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new kind of AI that plays chess-like games better than regular computers. It’s called Xiangqi Structurally Variable, or XQSV. This AI changes its internal structure to play like real people do. The researchers added special features to make it even better at guessing what moves someone will make next. They tested it and found that it was as good as a human player within certain skill levels. They also showed that this AI is hard to tell apart from a real person playing chess. |
Keywords
* Artificial intelligence * Deep learning
