They?are also known as ngrams. Their use in machine translation might provide 
some inspiration for computer go.



As with many of the ideas mentioned in this discussion, I've played with it 
enough to say it's not low hanging fruit but I?don't necessarily believe?it's a 
dead end. (I am fairly disenchanted with using patterns as context, though.)

-?Dave Hillis


-----Original Message-----
From: Peter Drake <dr...@lclark.edu>
To: computer-go <computer-go@computer-go.org>
Sent: Fri, Sep 25, 2009 3:41 pm
Subject: Re: [computer-go] Generalizing RAVE





On Sep 24, 2009, at 8:45 PM, terry mcintyre wrote:


Indeed it is. How may a program reason about the order of moves? At higher 
levels of play, the order of moves is often crucial.?





I plan to try the following:



Store win and run counts for each move in the context of the two previous 
moves. This would accumulate results for "forced" move sequences, such as 
edge-of-board hanes. In terms of the GRAVE model I discussed earlier, this says 
two boards are similar if they have the same last two moves (regardless of what 
happened elsewhere on the board).




Two moves, rather than one, are necessary to chain together sequences of moves. 
Otherwise, consider this:




.#O.

.#Od

.cab




If # moves at a, then b is the correct response, followed by c, then d. 
However, if # plays directly at c, O should not respond at d.

?

Obligatory acronym: SAVE (Sequence RAVE).




My guess is that this will not work as well as RAVE. It just might work to 
combine this information (which in a sense remembers local searches) with the 
RAVE information (which finds "globally" good moves). Combining pure AMAF might 
help, too, as it would solve the "missing sibling information" problem I 
mentioned earlier.






Peter Drake

http://www.lclark.edu/~drake/









= 


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