Forrest, similar multi-level or hierarchical/partitioned search concepts
have been suggested by several people here over the years, myself
included many times. I first suggested a chunking probability based
search concept back in 1998.
I have long been an advocate of goal-directed hierarchical search for
Go, but haven't yet figured out how to make it work in practice. I
tried some things years before MC/UCT popped up without any real success.
There could perhaps be some promise in finding ways to combine some of
these multi-level ideas with MC/UCT search techniques.
I don't understand the follow-up to your post claiming that you can't do
this for these kinds of games because they are not forcing move
sequences. We're talking about the play-out part of the search used to
sample the game tree. Anything goes, right? Of course, whether any
particular play-out method helps or not is another question.
-Matt
Forrest Curo wrote:
It's the approach I believe to be more human-like. Not necessarily the
playing style.
Human beings "chunk".
What all this fuss suggests to me is a "meta-mc" program... You include
routines that work out good sequences, as a human would--and then you
have the random part of the program include the more promising
sequences, where applicable, as if they were individual moves.
Forrest Curo
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