Deceit boils down to assuming your opponent will misjudge the situation and make a mistake. In Chess or Go, computer players choose the best move assuming that the opponent won't make any mistakes (will never be fooled). There's no attempt to model a weaker opponent.
This is why computer players in Go tend to fall apart when they start losing. They don't see a path to victory against a perfect opponent, so all moves look the same.
A Go AI that can win from behind (given a handicap) might be an interesting challenge.
> In Chess or Go, computer players choose the best move assuming that the opponent won't make any mistakes (will never be fooled). There's no attempt to model a weaker opponent.
What I meant was there's no reason an AI couldn't try to fool a human in a perfect information context. For example, you could bait a (probably not world class though) opponent into capturing a piece which puts them in a bad position, where certain baits are more likely to work on humans than others. There's plenty of situations in real life where two people have the same information but one comes out on top because the other overlooked a detail.
> This is why computer players in Go tend to fall apart when they start losing. They don't see a path to victory against a perfect opponent, so all moves look the same.
Is this not a limitation of the AI though instead of something inherent to AI?
> A Go AI that can win from behind (given a handicap) might be an interesting challenge.
It's more of a testing limitation. These programs are only occasionally tested against humans. You need expert players to test, they're not available all the time, and it takes a long time to play a single game (unlike poker). You can use databases of online games but it won't tell you how the player would have responded to a different move 37.
If you're writing a program to beat anyone, there isn't much point in optimizing the algorithm for a specific opponent's weaknesses when there are improvements to make that would work against anyone and the improvement in strength can be evaluated using computer testing alone.
Work on exploiting specific opponents' weaknesses is typically done using much simpler games (check out what's been done for rock paper scissors).
This is why computer players in Go tend to fall apart when they start losing. They don't see a path to victory against a perfect opponent, so all moves look the same.
A Go AI that can win from behind (given a handicap) might be an interesting challenge.