# A Study of Solving Life-and-Death Problems in Go Using Relevance-Zone Based Solvers

arXiv: https://arxiv.org/abs/2512.21365 · HTML: https://mlsanity.com/paper/2512.21365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 23 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Accelerating Self-Play Learning in Go](https://mlsanity.com/paper/1902.10565/) (2019)
- [ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero](https://mlsanity.com/paper/1902.04522/) (2019)
- [Adversarial Policies Beat Superhuman Go AIs](https://mlsanity.com/paper/2211.00241/) (2022)
- [Are AlphaZero-like Agents Robust to Adversarial Perturbations?](https://mlsanity.com/paper/2211.03769/) (2022)
- [Accelerating and Improving AlphaZero Using Population Based Training](https://mlsanity.com/paper/2003.06212/) (2020)
- [A Novel Approach to Solving Goal-Achieving Problems for Board Games](https://mlsanity.com/paper/2112.02563/) (2021)
- [Multi-Labelled Value Networks for Computer Go](https://mlsanity.com/paper/1705.10701/) (2017)
- [A Local-Pattern Related Look-Up Table](https://mlsanity.com/paper/2212.13922/) (2022)

## Abstract (arXiv)

This paper analyzes the behavior of solving Life-and-Death (L&D) problems in the game of Go using current state-of-the-art computer Go solvers with two techniques: the Relevance-Zone Based Search (RZS) and the relevance-zone pattern table. We examined the solutions derived by relevance-zone based solvers on seven L&D problems from the renowned book "Life and Death Dictionary" written by Cho Chikun, a Go grandmaster, and found several interesting results. First, for each problem, the solvers identify a relevance-zone that highlights the critical areas for solving. Second, the solvers discover a series of patterns, including some that are rare. Finally, the solvers even find different answers compared to the given solutions for two problems. We also identified two issues with the solver: (a) it misjudges values of rare patterns, and (b) it tends to prioritize living directly rather than maximizing territory, which differs from the behavior of human Go players. We suggest possible approaches to address these issues in future work. Our code and data are available at https://rlg.iis.sinica.edu.tw/papers/study-LD-RZ.