Author: Martin Schubert
Date: 00:09:47 05/09/01
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On May 08, 2001 at 14:05:39, Gian-Carlo Pascutto wrote: >On May 08, 2001 at 10:43:41, Martin Schubert wrote: > >>How should this be possible? First you need a zero hypothesis, e.g. Fritz is as >>good as Junior. Okay, that's not the problem. But statistics is only possible >>when results are independent. When you're using booklearning, they're not >>independent. So you can't calculate a degree of confidence. > >I can try to offer 2 solutions, but I don't know if they are good enough > >a) assume booklearning has no influence on the zero hypothesis, i.e. >that the learning of Junior and Fritz is equally good. This sounds >reasonable, but may not be correct. > >b) assume the booklearning is part of the zero hypothesis, so that >the strength of a program is also determined by its book learning >abilities. > >If either of these fail, I would appreciate it if you could point >out why. This is not my area, but I'd like to learn more. > >-- >GCP Maybe the booklearning of two distinct programs is equal, so that in one specific match it doesn't matter. But generally booklearning matters. (regarding a). The problem with b): When you do statistics, you repeat one experiment a lot of times. You repeat the same experiment, which means that the result of every experiment is independent from every other experiment. Which means that the probability for program A winning the first game must be the same than winning the last game. So if you're playing a program with booklearning against one program without booklearning. So let the probability of winning the first game 30%. Because of the booklearning after a lot of games the probability improves to let's say 50%. So statistics doesn't work (maybe there is a way to examine this. But to do that you had to know exactly what influence the booklearning has on two specific programs). So the goal of booklearning is increasing the probability of winning, so if it works better (or worse) than your opposits booklearning, you can't do statistics. Martin
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