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

TL;DR

This paper investigates how state-of-the-art Go solvers using relevance-zone techniques analyze Life-and-Death problems, revealing their strengths and limitations compared to human solutions.

Contribution

It introduces a relevance-zone based approach to analyze Go L&D problems and uncovers solver behaviors, including pattern recognition and decision biases, with insights for future improvements.

Findings

Solvers identify critical relevance-zones in each problem.

Discovery of rare and unique pattern solutions.

Differences between solver answers and human solutions.

Abstract

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.