Re: [computer-go] random evaluation results

[Don Dailey <drd@xxxxxxx>]

Here are some different results.  The only difference is that the
program calls the (random) evaluator after 2 passes inside the search.

I still find it amazing that random evaluation performs so well with
extra depth even though I knew of this finding before.  I just wasn't
sure how much of an effect it would have in GO.

Will test with bigger boards next.  Will it be better, worse or about
the same?  Any predictions?

Would anyone be willing to verify my results using the same
parameters?  Gunnar is doing something similar, but he is using a
whole different methodology which would take me some time to
duplicate.


- Don





 r2    51.700   1000 games
 r3    71.300   1000 games
 r4    73.400   1000 games
 r5    81.000   1000 games
 r6    91.000   1000 games
 r7    85.841    113 games



   >  I have no clue about what's going on at depth 7.   I cannot find a bug
   >  that would explain this.

   But I think it has something to do with not ending the game INSIDE the
   search after N passes ...  


      I ran several games with random evaluation and have some results to
      report.

      boardsize:   5
	   komi:   0.5
	     ko:   simple
      game over:   after 2 passes (but not inside search)
	   eval:   #define EVAL (8192 - (rand() & 0x3fff))
	scoring:   chinese (tromp/taylor) - no dead stone resolving.

       r2    44.300   1000 games
       r3    62.200   1000 games
       r4    68.800   1000 games
       r5    73.000   1000 games
       r6    92.200   1000 games
       r7    38.616    549 games


      r2 is a 2 ply search, r3 is 3 ply etc.  All players are playing
      against r1 which is a 1 ply search which is the equivalent of simply
      playing a random legal move.

      I have no clue about what's going on at depth 7.   I cannot find a bug
      that would explain this.

      - Don
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