Chess research mapping highlights gaps in AI strategic reasoning

A new systematic mapping study published on arXiv analyzes recent research into strategic reasoning, focusing on chess as a model domain. The study categorizes research across human players, traditional chess engines, neural networks, and large language models (LLMs). It reveals that while most research concentrates on situation assessment and action selection, areas like explicit planning, explanation, and human-AI collaboration remain less explored. The paper suggests future research directions in these under-explored areas, emphasizing the potential of chess to bridge cognitive and computational perspectives on strategic reasoning. AI

IMPACT Identifies key areas for future AI research in strategic reasoning, particularly in planning and human-AI collaboration.

RANK_REASON The item is a research paper published on arXiv detailing a systematic mapping study of existing research. [lever_c_demoted from research: ic=1 ai=1.0]

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