[Computer-go] semeai example of winning rate

[Aja]

hi Michael,

I agree with your views. Playout policy is rather important. I will try 
Professor Drake's "last good reply" in the playout and report the result 
next week.

Aja

-----原始郵件----- 
From: Michael Williams
Sent: Thursday, January 20, 2011 10:44 AM
To: computer-go at dvandva.org
Subject: Re: [Computer-go] semeai example of winning rate

On Wed, Jan 19, 2011 at 9:12 AM, Brian Sheppard <sheppardco at aol.com> wrote:
> The risk to scalability is that we will bias the search by focusing on
> variations that a blitz program cannot discover, but a massively scalable
> system could.

> Another possible instance: Pachi's playout policy. Pachi has conditioned
> each generator in the playout policy on a probability weight. For example,
> the rule that says "play around the last point if you see this pattern" is
> now only executed with a certain probability. IIRC, they report that
> executing each rule with 90% probability is marginally better than using
> 100%. I am pretty sure that deep and shallow searchers can differ on this. 
> A
> deep search can afford to explore because the MCTS tree is large and will
> sort things out, whereas a shallow tree is better off gambling that its 
> rule
> is correct.

A deep and long search has many short and shallow searches near the
fringe of the tree.

It seems to me that improvements in playout policy would apply to any
time control.  But perhaps in-tree heuristics should be dependent on
the number of visits to the node.
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