发表机构
University of Bologna; University of Molise（博洛尼亚大学; 莫利塞大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究系统映射84个国际象棋研究家族，发现文献集中于评估与行动选择，而规划、解释、元认知和人机协作不足，提出混合系统差异及人机协同评估问题，并指明未来研究方向。
AI 中文摘要
国际象棋长期以来一直是研究搜索、专业知识、决策制定和人工智能的模型领域。大型语言模型（LLMs）的出现重新凸显了国际象棋作为受控环境在研究策略推理以及比较人类与人工决策方面的相关性。我们提出了一项系统映射研究，涵盖近期涉及人类棋手、经典国际象棋引擎、神经网络和强化学习系统、LLMs以及混合方法的研究。最终映射包含84个核心研究家族，根据代理类型、策略推理阶段和评估维度进行分类。该映射揭示出文献高度集中于情境评估、评估和行动选择，而显式规划、解释、元认知和人机协作仍较少被探索。LLM研究特别强调状态表示和泛化，而基于事实的解释在将语言模型与引擎、专家知识或其他外部结构相结合的混合方法中更常出现。出现了两个映射所汇总而非解决的区分：混合系统在异构能力组合的地点和时间上有所不同，且显示人类表现提升的评估并不因此确立人机协同效应。我们提出这两点作为映射框架的扩展。我们认为国际象棋为策略推理的认知和计算视角之间提供了有用的桥梁，并确定显式规划、基于事实且忠实的解释、元认知校准和人机互补性作为未来研究的方向。
英文摘要
Chess has long served as a model domain for studying search, expertise, decision-making, and artificial intelligence. The emergence of large language models (LLMs) has renewed the relevance of chess as a controlled environment for investigating strategic reasoning and comparing human and artificial decision-making. We present a systematic mapping study of recent research spanning human players, classical chess engines, neural and reinforcement-learning systems, LLMs, and hybrid approaches. The final map comprises 84 core study families, classified according to agent type, strategic-reasoning stages, and evaluation dimensions. The map reveals a literature strongly concentrated on situation assessment, evaluation, and action selection, while explicit planning, explanation, metacognition, and human--AI collaboration remain less explored. LLM research places particular emphasis on state representation and generalization, whereas grounded explanation appears more frequently in hybrid approaches combining language models with engines, expert knowledge, or other external structures. Two distinctions emerge that the map aggregates rather than resolves: hybrid systems differ in where and when heterogeneous capabilities combine, and evaluations that show improved human performance do not thereby establish human--AI synergy. We propose both as extensions of the mapping framework. We argue that chess provides a useful bridge between cognitive and computational perspectives on strategic reasoning, and identify explicit planning, grounded and faithful explanation, metacognitive calibration, and human--AI complementarity as directions for future research.
CommentsUnder review; replication package available at https://doi.org/10.5281/zenodo.22695754
