[Computer-go] Evaluating improvements differently

Don Dailey dailey.don at gmail.com
Thu Apr 7 12:33:36 PDT 2011

On Thu, Apr 7, 2011 at 2:27 PM, Álvaro Begué <alvaro.begue at gmail.com> wrote:

> Hi,
> I haven't spent any time in go programming recently, but a few months
> ago I thought of a method to evaluate proposed improvements that might
> be much better than playing a gazillion games. A search results in two
> things: A move and a probability of winning (or a score that can be
> mapped into a probability of winning, but let's ignore that issue for
> now). Evaluating whether the moves picked by a strategy are good is
> really hard, but evaluating whether the estimate of a probability of
> winning is a good estimate seems much easier.
> For instance, take a database of games played by strong players.
> Extract a few positions from each game. Run your engine for some fixed
> amount of time on each position, and measure how well it predicted the
> winner after each position (cross entropy is probably the correct
> measure to use). Do this before and after the proposed modification
> and compare.
> Of course one has to be careful to pick reasonably well-played games
> (games played by top engines with more time per move than you'll use
> to evaluate your engine seems good enough, and will result in a much
> cleaner database than collecting games played by humans) and to have a
> large enough and varied enough set of positions. Also, one should
> worry about over-fitting for those particular positions, but one could
> use another set of positions for confirmation. These problems all seem
> manageable to me.
> It is possible that certain improvements can really only be measured
> by playing games (time control comes to mind), but I would imagine
> that for a large class of things this procedure can give you useful
> results in much more reasonable times.
> Your thoughts are appreciated.

I think this should be tried,  but I also believe you will be bitterly
disappointed.   The holy grail for me is to find some way to quickly test
versions and I have found that NO test does that better than playing games.
    The problem is that a real game is full of complex variables that
interact in strange and wonderful ways.

The reason a program wins or loses games is not as simple as just saying it
played a bad move,  or it failed to see this or that .   It's rather more
like the stock market.    Have you noticed that at the end of the day
someone reports on the stock market and always gives a reason why this or
that stock (or the entire nasdaq) rose or fell?     They like to pretend
they understand why but in reality it's just a wild guess.       It's that
way with why you win and lose games.   The reasons are very complicated -
you cannot just count the good moves and bad moves to get a global "score"
nor can you construct a good problem suite that will tell you how good your
program is (unless you are willing to accept about 2 or 3 Dan worth of

I am always interested in ideas like what you propose here and I think it
should be experimented with - I'm just very skeptical.   Been there, done

Years ago there were tests based on counting how many moves a player made
that matched games of top grandmasters.  You would hide the answer,  pick a
move, then see if it matched the move "Bobby Fischer" would play.    Then
your skill would be computed based on how many of these you got "right."
 However recent studies show that 2 equally stronger players (computers in
this case) can vary significantly in choice of moves and in fact a
similarity test was constructed that showed that different equally strong
programs played much more differently than 2 versions of the SAME program
where one version was older and significantly weaker.     So the choice of
move had a lot more to do with style than it did strength.    That is the
very opposite of most peoples intuition.

A program can play very well but be weak because of the occasional horrible
move,   or it can play very "average" without making major errors but still
missing a lot of "brilliancies."    There is complex interaction here that
determines the actual RESULTS it will get in competition.

However I still like your idea and if you (or anyone else) tries it I am
very interested in your experiences with it.    I am always hoping something
better will come along ...

By the way,  there is also the notion of weighted problem sets.    I think
your idea has similarities.   You try to solve problems but some problems
get much different "scores" than others.    This is an attempt to improve
naive problem testing.   Another idea that has been tried is to test as many
programs as possible of widely varying strength on a zillion problems and
then fit a scoring method that predicts strength based on this.


> Álvaro.
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> Computer-go at dvandva.org
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