A Study of Solving Life-and-Death Problems in Go Using Relevance-Zone Based Solvers
- URL: http://arxiv.org/abs/2512.21365v1
- Date: Tue, 23 Dec 2025 15:47:34 GMT
- Title: A Study of Solving Life-and-Death Problems in Go Using Relevance-Zone Based Solvers
- Authors: Chung-Chin Shih, Ti-Rong Wu, Ting Han Wei, Yu-Shan Hsu, Hung Guei, I-Chen Wu,
- Abstract summary: 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.<n>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.
- Score: 14.60765771996139
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- To Search or Not to Search: Aligning the Decision Boundary of Deep Search Agents via Causal Intervention [61.82680155643223]
We identify the root cause of misaligned decision boundaries, the threshold determining when accumulated information suffices to answer.<n>This causes over-search (redundant searching despite sufficient knowledge) and under-search (premature termination yielding incorrect answers.<n>We propose a comprehensive framework comprising two key components. First, we introduce causal intervention-based diagnosis that identifies boundary errors.<n>Second, we develop Decision Boundary Alignment for Deep Search agents (DAS)<n>Our DAS method effectively calibrates these boundaries, mitigating both over-search and under-search to achieve substantial gains in accuracy and efficiency.
arXiv Detail & Related papers (2026-02-03T09:29:06Z) - Learning the Boundary of Solvability: Aligning LLMs to Detect Unsolvable Problems [51.62477754641947]
We propose UnsolvableQA and UnsolvableRL to solve feasible problems, detect inherent contradictions, and prudently refuse tasks beyond capability.<n>Specifically, we construct UnsolvableQA, a dataset of paired solvable and unsolvable instances derived via a dual-track methodology.<n>Building on this dataset, we introduce UnsolvableRL, a reinforcement learning framework with three reward components jointly accounting for accuracy, unsolvability, and difficulty.
arXiv Detail & Related papers (2025-12-01T13:32:59Z) - Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving [37.94354699202412]
We show that higher solution divergence is positively related to better problem-solving abilities across various models.<n>We propose solution divergence as a novel metric that can support both SFT and RL strategies.
arXiv Detail & Related papers (2025-09-26T15:27:50Z) - HARDMath2: A Benchmark for Applied Mathematics Built by Students as Part of a Graduate Class [27.93059568425132]
HARDMath2 is a dataset of 211 original problems covering the core topics in a graduate applied math class.<n>This dataset was designed and verified by the students and instructors of a core graduate applied mathematics course at Harvard.<n>We build the dataset through a novel collaborative environment that challenges students to write and refine difficult problems consistent with the class syllabus.
arXiv Detail & Related papers (2025-05-17T00:52:49Z) - Matching Problems to Solutions: An Explainable Way of Solving Machine Learning Problems [1.7368964547487398]
Domain experts from all fields are called upon, working with data scientists, to explore the use of ML techniques to solve their problems.
This paper focuses on: 1) the representation of domain problems, ML problems, and the main ML solution artefacts, and 2) a matching function that helps identify the ML algorithm family that is most appropriate for the domain problem at hand.
arXiv Detail & Related papers (2024-06-21T21:39:34Z) - Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners? [140.9751389452011]
We study the biases of large language models (LLMs) in relation to those known in children when solving arithmetic word problems.
We generate a novel set of word problems for each of these tests, using a neuro-symbolic approach that enables fine-grained control over the problem features.
arXiv Detail & Related papers (2024-01-31T18:48:20Z) - Responsible AI (RAI) Games and Ensembles [30.110052769733247]
We provide a general framework for studying problems, which we refer to as Responsible AI (RAI) games.
We provide two classes of algorithms for solving these games: (a) game-play based algorithms, and (b) greedy stagewise estimation algorithms.
We empirically demonstrate the applicability and competitive performance of our techniques for solving several RAI problems, particularly around subpopulation shift.
arXiv Detail & Related papers (2023-10-28T22:17:30Z) - Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problem [68.07116119373565]
The Gromov-Wasserstein Optimal Transport (GWOT) problem has attracted the special attention of the ML community.<n>We crash-test existing continuous GWOT approaches on different scenarios, carefully record and analyze the obtained results, and identify issues.<n>We propose a new continuous GWOT method which does not rely on discrete techniques and partially solves some of the problems of the competitors.
arXiv Detail & Related papers (2023-03-10T15:21:12Z) - A Novel Approach to Solving Goal-Achieving Problems for Board Games [18.627167345021835]
This paper first proposes a novel RZ-based approach, called the RZ-Based Search (RZS) to solving L&D problems for Go.<n>RZS tries moves before determining whether they are null moves post-hoc.<n>We also propose a new training method called Faster to Life (FTL), which modifies AlphaZero to entice it to win more quickly.
arXiv Detail & Related papers (2021-12-05T13:23:10Z) - A Mutual Information Maximization Approach for the Spurious Solution
Problem in Weakly Supervised Question Answering [60.768146126094955]
Weakly supervised question answering usually has only the final answers as supervision signals.
There may exist many spurious solutions that coincidentally derive the correct answer, but training on such solutions can hurt model performance.
We propose to explicitly exploit such semantic correlations by maximizing the mutual information between question-answer pairs and predicted solutions.
arXiv Detail & Related papers (2021-06-14T05:47:41Z) - Cross-Domain Generalization Through Memorization: A Study of Nearest
Neighbors in Neural Duplicate Question Detection [72.01292864036087]
Duplicate question detection (DQD) is important to increase efficiency of community and automatic question answering systems.
We leverage neural representations and study nearest neighbors for cross-domain generalization in DQD.
We observe robust performance of this method in different cross-domain scenarios of StackExchange, Spring and Quora datasets.
arXiv Detail & Related papers (2020-11-22T19:19:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.