Hey Marc, It is a common question against supervised learning. I recommend reading about bias-variance dilemma.
http://en.wikipedia.org/wiki/Bias-variance_dilemma Aja 2014-05-19 19:10 GMT+01:00 Marc Landgraf <[email protected]>: > Hi, > Today I had an interesting discussion about bots learning from expert > (Pro/strong KGS) games to prebias the tree search and/or (soft-)prune parts > of the tree. > Point was, that playing situational moves out of their usual context can > throw the bot off, and force it to 'look' into the wrong direction first. > No doubt, the bot can recover from this misjudgement with some playouts, > but it is still first send into the wrong direction. > Example: Imagine cutting a onespace jump. The bot, looking into it's > pattern database, will usually only find this situation, when this move is > somehow reasonable. In those cases, often the answer is difficult and > sacrifices have to be made. But the most punishing answers won't even be in > his database, as he has never seen the case in a pro game, where the move > is clearly punishable. But instead the bots tree search will first check > the standard answers for difficult cases instead of the clear punishments. > It may happen, that the bot then chooses a submissive answer (because that > is what usually happens to the reasonable version of the move) instead of > the good move/punishment. > Surely this example isn't perfect, but I hope it illustrates the problem, > I see. Similar things happen with joseki, which can be played correctly, > but most likely not properly punished, as the wrong variations are not > available in the database, except when they are contextually possible. > > What makes this problem even worse, is that with the standard methods of > playtesting it won't be noticed. In tests against (own or other) engines, > if both use a similar database, those moves won't appear out of context. > And even playtesting against random opponents on KGS won't show those > weaknesses clearly, as even if single players identify those weak spots, > their number of games won't be significant usually. I'm not even sure, how > one could systematically check for such misjudgements by the bot. > > Overall, I'm in no way against learning from expert games, and I think > there is no doubt, that it is a significant source for improvement of the > bots. But the question remains, how those weaknesses could be fixed. The > bots have learned how to answer proper play. But how do they learn to > answer unusual/bad play? > > Marc > > > > _______________________________________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go >
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