I was not feeling well today, so I didn't go to the go club.
I have long avoided playing computer games because they are time sinks, and computer go proves to be as addictive as any other game. I am still not playing human players online as often as I should, but I am definitely improving against MFOG.
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Progress at 4 months )
There have only been two computer games I ever found challenging, and even these were only when set on a high level: Chess and epic strategy games like Total War or Crusader Kings II. The former is obvious; the latter may be my fault for lack of experience, or because of how complicated the game is, so that even I cannot really look ahead enough.
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I have to wonder though, is it that Go is so much more complicated than Chess so it takes too much or effort to make a good AI, or is it simply that no one's bothered trying making a really good AI for Go.
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And I would disagree that Go is less complicated than those games. It's a 19x19 board and you can place a stone anywhere there isn't already a stone. If you try to brute-force calculate variations (which is what the chess computers do) it gets really complicated really fast. I don't know that there as many options in the computer games.
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Besides the depth problem that tealterror0 mentioned (just using brute-force look-ahead algorithms, go is exponentially more complex than chess), there is the fact that go is strategically more complex than chess as well. Heuristics for evaluating the value of a move in chess can use fairly well-defined metrics (material, positional advantage, etc.) based on the current state of the board. But in go, a stone played on one side of the board can have implications on the other side of the board 20 moves later, and any move might be good for profit but poor for influence, or vice versa, which means whether or not it's a good move depends on which strategy the player is pursuing.
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