The research investigates the balance between immediate opportunities and long-term goals in chess by analyzing human versus human and AI versus AI games. Using a novel network-based metric to measure piece-to-piece interactions, the study quantifies strategic tension throughout the game. Results show that top AI players sustain higher levels of strategic tension over extended periods compared to elite human players, indicating a tolerance for more complex and interconnected positions. The study also finds that cumulative tension correlates with algorithmic complexity in AI and increases sharply with human expertise at around 1600 and 2300 Elo ratings. This suggests that human players adapt their strategies to manage cognitive limitations by limiting tension and complexity, whereas AI can maintain a balance between offensive and defensive tactics without such constraints. These differences reveal distinct approaches to strategic decision-making between humans and AI. The findings have broader implications for deploying AI in complex, strategic environments beyond chess, where managing tension and complexity is critical. Understanding these dynamics can inform the development of AI systems that complement human cognitive strategies or operate effectively in high-stakes decision-making contexts. Future research could explore how these insights translate to other domains requiring strategic planning and tension management.
👉 Pročitaj original: arXiv AI Papers
