Author: Edward Screven
Date: 12:45:32 03/31/98
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On March 30, 1998 at 23:18:54, Robert Hyatt wrote: >On March 30, 1998 at 17:36:03, Edward Screven wrote: > >>at a minimum, i think you would want to know the overall strength >>of each member. if your program wins against a weak player, that's >>not very interesting, and neither is losing to a very strong player. >>in both cases, whatever experience you glean from such games should >>have less weight than extracts from games lossed to weak players or >>won against strong players. >> >>a half-assed way to do this on a server would be to only learn from >>losses, which makes since if you think your program is stronger than >>most opponents. > > >Learning from losses only won't work. I did it. You simply learn that >*no* book move is playable, because you will likely lose one game in >each >opening if you play strong enough players. You also have to fold in >wins >and "equal games" or run the risk of having no book. Think about the >limit of the equation where all scores are "losses"... :) > of course, good point. but do you weight the recorded results by opponent strength? crafty would most likely beat me even after opening with an awful line. using a victory against me to encourage play of that line in general would be a mistake. i have implemented simple learning in my own program. since i don't play on servers, it has only a small roster of opponents, so i haven't needed to attack this problem. but if the time comes, i might try something like this for book learning: + for each opponent, estimate a probability of win, loss, and draw (call them Pw, Pl, and Pd.) + instead of just accumulating a unit win, loss, or draw at each node of the book, accumulate -log2(Pw), -log2(Pl), or -log2(Pd) depending on the outcome. in other words, estimate how many bits of information each outcome carries, and use that as the book learning weight.
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