[Computer-go] Anomalies in MCTS
sheppardco at aol.com
Tue Feb 5 18:12:24 PST 2013
Pebbles has seki understanding built into playouts, so Pebbles plays the
correct move from the start, with over 99% win rate.
From: computer-go-bounces at dvandva.org
[mailto:computer-go-bounces at dvandva.org] On Behalf Of Cameron Browne
Sent: Monday, February 04, 2013 7:30 AM
To: computer-go at dvandva.org
Subject: Re: [Computer-go] Anomalies in MCTS
You might try some of the RAVE counter-examples, such as the following
position from Martin Mueller (White to move):
It's a seki position in which FUEGO chooses the correct move B2 without
RAVE, but the incorrect move D9 with RAVE enabled.
Not sure whether this will translate to the bizarre MC behaviour that you're
looking for, but it's worth a shot. As I understand it, RAVE fails here
because the key move wins if played immediately but loses in most cases if
played further down the line, hence the accumulated RAVE values initially
disguise the actual first-move value. Perhaps you might see a similar effect
related to the number of MC runs, i.e. additional (wrong) information hides
the correct value?
> Date: Mon, 04 Feb 2013 10:51:11 +0100
> From: "Ingo Alth?fer" <3-Hirn-Verlag at gmx.de>
> To: computer-go at dvandva.org
> Subject: [Computer-go] Anomalies in MCTS
> Message-ID: <20130204095111.108540 at gmx.net>
> Content-Type: text/plain; charset="utf-8"
> Wesley Turner and me are analysing "pure Monte Carlo" (without tree
> search component) in (artificial) simple games. Let MC(k) be the
> version where each candidate move gets exactly k simulations. We found
> examples where for instance MC(8) performs better than MC(16), but for
> k to infinity pure Monte Carlo plays perfectly.
> Question to the MCTS programmers (in Go): Do you have example
> positions where MCTS makes good move proposals for short run times and
> bad proposals for medium long run times? (Of course for time to
> infinity it should converge to good moves.)
> Cheers, Ingo.
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