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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Ataraxos, an AI system described in a 2026 Nature paper, beat Stratego champion Pim Niemeijer 15–1, with four draws, in a 20-game series played over three weeks. Counting each draw as half a win, the authors report an 85% effective win rate. This is the result behind the “greatest player” headline—not DeepNash’s earlier online ranking, which came from a different evaluation against a different pool of opponents.
What happened in the Ataraxos–Pim Niemeijer match?
The 2026 paper reports that Ataraxos won 15 games, lost one and drew four against Niemeijer. The series took place over three weeks. Its stated 85% effective win rate counts a draw as half a win: 15 wins plus two half-wins from draws equals 16 effective wins out of 20 games. Nature paper on Ataraxos
The paper describes Niemeijer as Stratego’s most decorated player of all time. That makes this a notable direct contest between an AI and an elite human player. It does not, by itself, establish that AI has ended human competition in Stratego or in board games generally.
How does this compare with DeepNash?
DeepNash, developed by Google DeepMind, reached expert-level play and an all-time top-three ranking on the online Gravon platform in results reported in 2022. DeepMind said it won 84% of its matches against top expert humans there and more than 97% against leading Stratego bots. Those figures describe DeepNash’s reported Gravon and bot evaluations—not a match against Niemeijer. Google DeepMind’s 2022 DeepNash account
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The 2026 paper adds important context to the Gravon result. It says the platform’s player pool had diminished, and that the opponents DeepNash faced there were far from top-human level and did not know they were playing a bot. The paper retrospectively counts DeepNash’s 2022 human-evaluation record as 42 wins in 50 games. 2026 paper’s account of DeepNash evaluations
At a 2023 Stratego World Championship demonstration, DeepNash recorded 19 wins and nine losses. The paper says most of the highest-ranked players who faced it beat it, including Niemeijer. That demonstration is another distinct evaluation; it should not be merged with either the Gravon record or Ataraxos’s later series.
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| System and evaluation | Reported result | What the result represents |
|---|---|---|
| DeepNash vs. leading bots | More than 97% wins | Matches against leading Stratego bots, as reported by DeepMind in 2022. Source |
| DeepNash vs. human players on Gravon | 84% wins; top-three all-time ranking | DeepMind’s 2022 report. The 2026 paper later qualifies the opponent pool and awareness. DeepMind; 2026 paper |
| DeepNash at 2023 championship demonstration | 19 wins, 9 losses | Demonstration games; the paper says most of the highest-ranked participants who played it beat it. 2026 paper |
| Ataraxos vs. Pim Niemeijer | 15 wins, 1 loss, 4 draws; 85% effective win rate | 20-game series over three weeks; draws count as half a win. 2026 paper |
| Ataraxos vs. attendees at the 2025 Stratego World Championship | 38 wins, 2 losses, no draws; 95% effective win rate | A 40-game demonstration against attendees, not the tournament itself. 2026 paper |
The percentages are not a head-to-head comparison: the systems faced different opponents under different conditions, and the results came from platform matches, demonstrations and a series against one named player. The available reports do not establish a direct DeepNash-versus-Ataraxos match.
Why is Stratego a difficult game for AI?
Stratego is a two-player game of hidden information. Each player secretly arranges their pieces, then tries to infer the opponent’s ranks through encounters and play. A strong player must balance long-term plans with bluffing, information gathering and manoeuvring. Google DeepMind’s explanation of Stratego and DeepNash
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Unlike games where both players can see the full position, Stratego requires decisions under uncertainty about the opponent’s pieces and intentions. An AI cannot simply calculate from a complete board state; it must also manage what the opponent can infer and learn from what happens during a match.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How did DeepNash learn Stratego?
DeepNash used model-free deep reinforcement learning with self-play and a game-theoretic approach. DeepMind reported that it learned without search, varied its initial deployments and randomized among actions that appeared equivalent. That unpredictability matters in a game where a pattern in placement or play can give an opponent useful information. DeepMind’s DeepNash account
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DeepMind quoted Vincent de Boer, a co-author of the paper and a former Stratego world champion, saying: “The level of play of DeepNash surprised me. I had never heard of an artificial Stratego player that came close to the level needed to win a match against an experienced human player. But after playing against DeepNash myself, I wasn’t surprised by the top-3 ranking it later achieved on the Gravon platform.” DeepMind’s interview with Vincent de Boer
That description concerns DeepNash; it should not be treated as a technical account of Ataraxos. The 2026 match result establishes Ataraxos’s performance against Niemeijer, but the figures above alone do not explain its architecture or training method.
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Does this mean humans can no longer compete in Stratego?
No. Ataraxos’s result is strong evidence that an AI can beat an exceptionally decorated Stratego player in a sustained series. It is not evidence that every elite human would receive the same result, that humans have been surpassed in every format, or that the game’s competitive scene has ended. The 2025 championship-attendee games were a demonstration, not a world-championship tournament result, and the DeepNash evaluations show how much opponent pool and match conditions affect interpretation.
The defensible conclusion is narrower and more significant: Ataraxos beat Niemeijer in a 20-game series, while DeepNash had earlier demonstrated strong play and earned a top-three Gravon ranking under a separate evaluation. Those milestones belong to different systems and should not be conflated.
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