6 ms·

I don't know much about Go but I thought a comment about AlphaGo was interesting that it played in a way to marginally beat the player, which was different that

• gertef 10y ago
  Humans play for a large lead because they don't have enough memory/power to accurately estimate the value of their positions, so they play for a buffer -- AlphaGo has higher confidence in its valuation, so it can play it closer -- ~85% confidence of winning by 5 stones (with room for error) vs 99% chance of winning by 2 stones
  • hyperpape 10y ago
    I believe that accuracy/"self-confidence" is part of it. However, I think it's also the case that AlphaGo has a monte carlo tree search in addition to the neural net, so it sometimes plays more conservatively than it needs to because it overweights obscure possibilities ("defending here is not necessary, but by doing so, I prevent some number of playouts where I play a dumb move and lose, and I can still win even if I defend"). Humans do the same thing, playing conservatively in a situation where they're far enough ahead. The difference is that a human sometimes looks at a move and says "This move works, and gains points. There is no risk." For a bot using MCTS, everything is a probability.
  • taejo 10y ago
    Another factor is that many micro-endgame sequences simply have a "correct answer" that loses the least points. Any human who's played Go for more than a few months knows these sequences, and if they choose to answer a certain move, will prefer the answer which is "always correct" to another move which would also win the game. This naturally leads to the winning player preserving their margin even when they could throw it away, while the machine has no such bias, and will just as happily throw away the margin as preserve it.
• drcode 10y ago
  I think this is somewhat incorrect- The creators of AlphaGo made it clear that their system does not take the opponent into account at all, it just answers the question "What is the strongest move right now?" and plays that move, without taking the opponent into account. In other words, it does not have any mental model of the opponent. However, you are correct insofar that it doesn't care about winning by large margins, it prefers winning by smaller margins if it can achieve this with a higher win probability.
  • smallnamespace 10y ago
    A perhaps more accurate way to say it is that AlphaGo's models its opponents as a copy of itself.
    • conanbatt 10y ago
      So do human players, with rare exceptional ocassions
      • iopq 10y ago
        Playing trap moves is not that rare and it shows that you expect the opponent not to know a complicated variation.
        • conanbatt 10y ago
          Strong players dont play "trap moves"
          • iopq 10y ago
            Who said anything about strong players?
            • smallnamespace 10y ago
              > AlphaGo
• conanbatt 10y ago
  Thats not true. Go is a game where the safety of the lead is more important than the amount at all times, and its taken into account strategically all the time. What alphago showed however, is that once in the lead it made mistakes: small mistakes that didnt jeopardize the game, but lost the lead. If all paths lead to rome, it doesnt matter which is shorter. Humans however, always think of the best after safety. What was terribly cruel to see as a Go player was the computer playing poorly early on: showing that it already knew it was going to win.
  • thomasahle 10y ago
    Modern chess engines take a lot of effort to hide this problem. For example, an engine might know from a table that it can win a king+rook vs king+pawn endgame, and then throws away its second rook to reach such a position. Humans using the engine don't like this however, and so the authors build in heuristics to make it play more "humanly". Things like using scores for positions, rather than "chance of winning" is also largely for the sake of the users.
• tel 10y ago
  All players play to marginally beat their opponent. Playing too aggressively when you're in the lead is "losing a won game" because it gives opportunities for your opponent to create turnarounds. In the end of a well-played game the losing player will find that they perhaps get more points than they expect, but each time they win a tradeoff with a slight margin the position on the board becomes much more solid and fixed than it ought to be. A 10 point lead with a large variance consolidates toward an unfaltering 1/2 point lead. Indeed, commentary on Master (P)s games seemed to suggest this was exactly how endgames went. Early in the game, confidence in winning is usually correlated exactly with point margin... but at the same time, score in the early parts of the game is very hard to estimate. It's repeatedly noted that AlphaGo plays a very "influence oriented" game which means that it eschews confidently having many points for having lots of "power" on the board which will later translate to points. So, all together I'd say that AlphaGo plays very well here and doesn't have too much of an obvious computational bias. The one true oddity is once AlphaGo's internal win probability reaches 100% it starts playing idiotic moves. The reason is simple: it's just searching for moves which have the highest expected win rate and at this point nothing it can do is bad. It won't lose points or anything, but instead just plays moves that are obviously pointless.