Hi!
I have came upon an interesting trick for analyzing behavior changes
in noisy systems like game AI runs, that draws heavily on using some
abstract characteristics and visualizing them:
http://yieldthought.com/post/95722882055/machine-learning-teaches-me-how-to-write-better-ai
Has anyone tried something like this in Computer Go? It's been pretty
inspiring for me because I have always found finding bugs and
shortcomings in heuristics a really painful process.
One question is what game-continuous performance characteristics
to choose in Go. An obvious one is winrate, for reading measure
I guess also numbers of dead / unstable stones.
I guess I'll try to implement this for Pachi + gogui-twogtp
-debugtocomment, the tool itself can be engine-independent.
(Of course, given that we also simulate games during MCTS, this could
be brought into an even more interesting level.)
--
Petr Baudis
Life is short, the craft long, opportunity fleeting, experiment
treacherous, judgment difficult. -- Hippocrates
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