I did an experiment that looks rather similar. I generated patterns
and only kept the ones that had a minimum amount of 'urgency' and a
minimum number of occurrences. But I noticed two things when using
these patterns in a MC playout:
1) There are many important moves missing. Apparently they were not
picked up from the game-database even though the number of games is
in the 10-thousands.
2) When I used these patterns during simulation the playouts suddenly
look surprisingly like normal Go compared to random playouts.
However, the program started to play worse. Much worse. Even when I
let it compute as many playouts as a program without patterns.
The first observation made me wary to rely on harvested patterns. I
think it shows at least it needs to be used in combination with hand-
crafted patterns. Also it means that the fact you harvest a large
number of patterns isn't necessarily meaningful.
The second observation I think may have been caused by not enough
randomness. But that means I first have to find an answer to how much
randomness I need to put into the patterns. I'm first looking at this
question with some hand-crafted patterns to get a better handle on
this issue.
Mark
On 30-mrt-08, at 09:14, Jacques Basaldúa wrote:
Mark Boon wrote:
>There are 16 4-distance points, so if you spill ino that by one
> point you get 315 or a little over 14 million patterns. Multiplied
> by 3 for every don't-care point within less than 4 distance. Ouch.
True, but the number of patterns is learned automatically. When I
first learn the 55K+ games, there are so many patterns that I can
only create a pattern file of less than 2000 games. I create 32
such files an call "importance" the number of files in which each
pattern is found. (a value from 1 to 32). The number of patterns
are:
# of imp = 32 97132 (97132)
# of imp = 31 26493 (123625)
# of imp = 30 21460 (145085)
# of imp = 29 19335 (164420)
# of imp = 28 18415 (182835)
# of imp = 27 18703 (201538)
# of imp = 26 18619 (220157)
# of imp = 25 19345 (239502)
# of imp = 24 20390 (259892)
# of imp = 23 21611 (281503)
# of imp = 22 22959 (304462)
# of imp = 21 24675 (329137)
# of imp = 20 26808 (355945)
# of imp = 19 29081 (385026)
# of imp = 18 31938 (416964)
# of imp = 17 35319 (452283)
# of imp = 16 39188 (491471)
# of imp = 15 43899 (535370)
# of imp = 14 50391 (585761)
# of imp = 13 57259 (643020)
# of imp = 12 67062 (710082)
# of imp = 11 79013 (789095)
# of imp = 10 95292 (884387)
# of imp = 9 117109 (1001496)
# of imp = 8 147810 (1149306)
Depending on the threshold value used (and also the number
of times the pattern is seen) I can create databases from about
100K patterns to 1M patterns, more than that means including
patterns that are too seldom, their urgency information won't be
very accurate either due to the small sample size.
Jacques.
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