AI’s game-playing still has flaws, research shows

Many AI researchers describe game-playing as the "Formula 1" of AI: it's a controlled test environment with clear rules and clear success criteria. This paper uses that idea as a diagnostic, by studying a very simple game Nim, a children's matchstick game whose optimal strategy is known exactly.

Because the correct move is known for every position, we can measure whether an agent plays optimally across the state space. The research found that while small boards can work, despite heavy training agents show blind spots and can miss optimal moves, and performance degrades as the board grows, with predictions approaching random. This suggests impartial games often need analytic representations, not pattern learning.

What does this mean for gaming with machines?
Self-play AIs can be very strong, but in games where both players share the "pieces" and the winning strategy is an abstract arithmetic rule, pattern-recognition from raw positions may not be enough on its own.

Wider implications:
The results don't diminish the achievements of self-play AI in games like chess and Go. Rather, they help map where today's methods can struggle, and where more abstract representations or hybrid approaches may be beneficial. More broadly, it's a reminder that systems can perform well in common cases while remaining brittle in rare-but-important ones.

Dr Søren Riis Reader in Computer Science at Queen Mary University of London said: "Nim is a children's game with a complete mathematical solution, yet AlphaZero-style self-play can still develop blind spots-becoming competitive while missing optimal moves across many positions."

"This suggests that, for future work in AI, impressive performance alone is not proof that a system has learned the underlying principle: methods that capture abstract structure may be needed to reduce blind spots."

"Impartial Games: A Challenge for Reinforcement Learning " by Dr Bei Zhou, Research Associate at Imperial College, Londonand Dr Søren Riis, Reader in Computer Science at Queen Mary University of London, is published in Machine Learning.

DOI: https://doi.org/10.1007/s10994-026-06996-1