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Subject: Re: Opponent specific learning...

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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