michael13-Mar-2026
#AI’s #gamePlaying still has flaws: #AlphaZero-style self-play tested on #Nim
Despite heavy training, agents show blind spots and can miss optimal moves
AI’s game-playing still has flaws: AlphaZero-style self-play tested on Nim
Games are often called the ‘Formula 1’ of AI: clear rules, clear winners. AlphaZero-style algorithms learn by self-play: a neural network predicts moves and guides tree search. We tested this recipe on Nim, a simple children’s matchstick game that has been mathematically solved. Because the correct move is known for every position, we can measure whether an agent plays optimally across the state space. We find a gap: learning can work on small boards, but blind spots remain and performance degrades as the board grows, with predictions approaching random. This suggests impartial games often need analytic representations, not pattern learning.
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