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This research demonstrates a significant breakthrough in handling imperfect information tasks, where AI models must make optimal decisions without full visibility into an opponent's state. The new system not only defeats top human players in Stratego but also achieves this with lower computational costs and better generalization across different strategic games. Such capabilities are directly relevant to real-world applications involving hidden variables, including military strategy, business negotiations, and cybersecurity defense.

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This game-playing AI is the new champ at Stratego

MIT NewsSeptember 30, 20266 min read

Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.

A new AI system that excels at challenging games with hidden information could someday help human decision-makers select ideal strategies to outfox opponents in complicated situations like military maneuvers. Using advances in machine-learning, researchers from MIT, Carnegie Mellon University, New York University, and Stanford University developed an AI that defeated top-ranked human players of the board wargame Stratego by a large margin — something no AI system had been able to achieve. Stratego, a two-player game of imperfect information, in which the opponent’s piece identities remain hidden, is often used as a benchmark to test the strategic thinking abilities of powerful AI models. To build their model, the researchers combined efficient training algorithms with…

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