I’m working on chess computer opponents, and someone who tried them gave me a criticism I thought was interesting. A bot moved its queen away from an attacking rook. On the next move, it put the queen right back in danger. The player said: “Maybe blunders need to feel reasonable. They should have intention behind them.” That stuck with me. It’s not that a human would never make that mistake. It’s that the second move seemed to forget why the first one happened. Our setup mixes Maia and Stockfish with some custom move-selection rules. It doesn’t always produce a consistent opponent. Sometimes it feels like two different players taking turns. Part of the problem might be how differently people approach the board. We calculate, but we also recognise familiar shapes, notice an open line or a weak square, and focus our attention on particular areas. We carry an idea across several moves: get a knight onto that square, trade off that bishop, build an attack on that side. That seems different from searching possible continuations and choosing a move from their evaluations. Even if an engine keeps choosing good moves, it doesn’t necessarily reproduce the same attention, expectations or attachment to a plan. Someone might get excited about an attack and overlook a defender. Or spend three moves setting something up and keep going after it stops making sense. The mistake comes from what they’re focused on, not just a random decision to play worse. I wonder whether modelling some of that would help. But you could easily end up with a bot that predictably misses bishops or stubbornly follows every plan, which wouldn’t feel right either. Has anyone worked on this, in chess or other games? How do you give an AI believable blind spots and some continuity of thought without making it feel like it’s deliberately letting you win? submitted by /u/space64-llc
Originally posted by u/space64-llc on r/ArtificialInteligence
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