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cloud.google.com·6h ago
AI Research
arXiv CS.AI·25d ago·5 min read
What Counts as Strategic Reasoning? A Systematic Mapping of Chess Research on Humans, Engines, and Language Models
TL;DR
A systematic mapping study of chess research reveals a focus on situation assessment and action selection, while areas like planning and human-AI collaboration are underexplored. The study categorizes 84 research families across various agent types and strategic reasoning stages.
✦ Why It Matters
Explore hybrid AI systems in chess to enhance strategic reasoning capabilities in your projects.
Key Takeaways
- Investigate the role of explicit planning in AI systems.
- Focus on grounded explanations in hybrid AI models.
- Explore metacognitive strategies for improved decision-making.
- Assess human-AI synergy in chess applications.
Full Summary
Chess has been a key domain for studying decision-making and artificial intelligence. This mapping study analyzes recent research on human players, classical chess engines, neural networks, reinforcement learning systems, and large language models (LLMs).
It identifies 84 core study families, categorized by agent type and strategic reasoning stages. The findings highlight a concentration on situation assessment, evaluation, and action selection, while areas such as explicit planning and human-AI collaboration remain less examined.
Notably, LLMs emphasize state representation and generalization, with hybrid approaches often providing grounded explanations. The study suggests that chess can bridge cognitive and computational perspectives, proposing future research directions in planning, explanation, and metacognition.