Re: computer-go: A problem with understanding lookahead

["Mark Boon" <tesuji@xxxxxxxxx>]

Well, your thought experiment is a good one in case of a reliable evaluation
but it's not necessarily applicable in the case of an unreliable evaluation.
Most search strategies rely on alfa-beta cut-off or something similar to cut
down the tree-size which means you need to use evaluations to determine what
to cut-off. If the evaluation is unreliable, the actual tree becomes just as
unreliable. I think David has written several times here that he experienced
that there's a point where more reading made it less reliable because you
give it more opportunity to construct a false search-tree.

This is not the case in Chess because it's so much easier to make a reliable
evaluation function.

Did anyone do any research on this? I mean, trying to quantify theoretically
how much the evaluation has to be wrong to get a less reliable tree at a
given depth. It might possibly be that it's only the case for really very
unreliable functions, which will of course be the case for Go. That would be
very useful information.

    Mark

From: Don Dailey <drd@xxxxxxxxxxxxxxxxx>
To: <computer-go@xxxxxxxxxxxxxxxxx>
Sent: Wednesday, January 17, 2001 3:16 AM
Subject: Re: computer-go: A problem with understanding lookahead


>
> Lookahead  is a simple  concept  but very  few  people seem  to have a
> significant grasp of what it is all about.
>
> In a  nutshell, if I have a  clearly defined goal  and a function that
> recognizes  it, then in  a 2 player  perfect information game like Go,
> Chess, Checkers, tic-tac-toe etc,  you can  try all possibilities  and
> discover that you  can either achieve your  goal or not.  There are no
> exceptions,  this always works,  as long as the   goal can be measured
> with 100% certainty.
>
> But with     imperfect evaluation  functions,    things are different.
> Whether you search  deep or shallow,  your end-node evaluation is just
> as imperfect.   So why does  passing it  down  the  tree makes it  any
> better?  Or does it?
>
> The answer is  that there  is  some magic   in the fact  that a  score
> actually survived the journey to the root node!  This single score had
> to  compete with a vicious adversary  (during the min-max process) who
> has  the opposite goal.  In  a nutshell, it  boils down  to this: It's
> better to  be happy 20 moves  from now, that it  is  to be happy right
> now!   That's the key to  mini-max  and why it  works.   It works with
> humans too, consider:
>
> Let's do a strange  thought experiment.  Suppose,  as a Go player, you
> had the  ability  to know exactly  how  you would feel  20 moves later
> about any move you make.  Let's say I like a certain move, but a voice
> in my head magically tells me that  20 moves later  I will not like my
> position.  Let's say this ability  is integrated in a nicely recursive
> fashion into your  brain, so that I know  that the  move I'm currently
> thinking of playing is the culprit, not one I  might play later (which
> is exactly what a search does.)  Would this ability  make you a better
> player?  In my  opinion it would make a  very good  player practically
> unbeatable, and a weak player much better.
>
> The "magic" seems to  be that if  you can survive  many moves  and you
> still  like your position (which  a search with an evaluation function
> proves because it  "knows" it will like  it's position n moves later),
> then it's probably a pretty good move.
>
> People used to wonder, in Chess, why just  doing a single extra ply of
> searching  was so powerful,  despite the fact that  it  SEEMED like we
> needed a whole lot more just to get a little improvement.  But this is
> why.  The "obvious" conclusions were completely wrong.  Even very weak
> players  often realize  after  seeing  the opponents  reply that  they
> "screwed up."  I do this  in Go myself.   If 1 ply error free searches
> were  built into the  human brain, every chess  player would improve a
> class.
>
> I think it's true of Go too.  However we have to assume a "reasonable"
> evaluation function and FULL WIDTH search for these things to be true.
> Unfortunately, anything more than a 1 or 2 ply FULL WIDTH search seems
> to be unreasonably difficult  in GO, if  you also require a reasonably
> good evaluation function!
>
>
> Don
>
>
>
>
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>
>    Hello all,
>
>    I have a serious problem with lookahead. I don't understand why it
works.
>    Why should the backed up values of a, say, 7 ply full width alpha-beta
>    minimax search be any better than the values my evaluation function
gives
>    me by simply evaluating the positions at ply 1. I can understand that
if
>    the end of the game is in sight then lookahead is useful. I can
>    understand  that if I use a quiesence measure or some such and only
>    evaluate positions when I know that my evaluation function will work
best
>    then lookahead is useful. But if neither of these is the case then does
>    lookahead help. Is there some way in which the backing up of values and
>    minimaxing somehow reduces the error in the evaluations.
>
>    I really am seriously stuck with this one.
>
>    Cheers
>
>    David
>
>
>