Author: blass uri
Date: 08:55:14 05/11/00
Go up one level in this thread
On May 11, 2000 at 11:39:57, Graham Laight wrote: >Here are my thoughts on the above subject. It's only a first draft - I reserve >the right to improve these diagrams in the light of people's comments! > >On the graphs below, the horizontal axis represents the breadth of knowledge >which is relevant to a position. The vertical axis represents depth of search in >ply. A "#" character indicates that the player has knowledge relavant to the >position at this point on the graph. > >The picture below represents the typical computer, with relatively little >knowledge, and no search extensions, searching to 10 ply: > > > ply |-------------------------------------------------------------| > | | >25 | | > | | >20 | | > | | >15 | | > | | >10 |#############################################################| > |#############################################################| >5 |#############################################################| > |#############################################################| > |-------------------------------------------------------------| > > Breadth of knowledge > >What this shows is that the computer has extremely good knowledge of what's >happening in the next 5 moves (1 ply = 0.5 moves), but very poor knowledge after >that. So - it can play good tactics, but make positional errors, because it >knows nothing of the long term consequences of its moves (also known as the >"horizon effect"). > >Now, here's a good human player's knowledge distribution: > > ply |-------------------------------------------------------------| > | # | >25 | # # | > | # # # | >20 | # # # # | > | # # # # # | >15 | # # # # # # | > | # # # # # ### | >10 | # # # # # # # # # # | > | # # # # # # # # # # # # # # # # # # # | >5 |# # # # # # # # # # # # # # # # # # # ## # # # # # # # # # #| > |#############################################################| > |-------------------------------------------------------------| > > Breadth of knowledge > >As you can see, our human friend can't see everything up to 5 plies, so he could >make a tactical error. However, because he has positional knowledge, and because >his experience allows him to visualise how the game might progress, he is able >to see a long way ahead, and avoid some poor positional avenues in the game. >However, there are, as you can see, gaps in his knowledge - and these gaps get >bigger the further ahead the search goes. > >Now, suppose our silicon friend is given some extra speed. The result may look >something like this: > > ply |-------------------------------------------------------------| > | | >25 | | > | | >20 | | > |#############################################################| >15 |#############################################################| > |#############################################################| >10 |#############################################################| > |#############################################################| >5 |#############################################################| > |#############################################################| > |-------------------------------------------------------------| > > Breadth of knowledge > >Now, Mr Silicon is more likely to win, because he has excellent coverage of >knowledge in areas where Mr Primate has relatively sparse knowlege. However, the >human might still win if the computer plays a move that leads to a place on the >graph where the human has some knowledge, but the computer doesn't (ie a poor >positional move). > >Now, instead of giving the computer extra speed, we'll give it extra knowledge >instead. The result might look as follows: > > > ply |-------------------------------------------------------------| > | # | >25 | # # # | > | # # # # # | >20 | # # # # # # | > | # # # # # # # # # # | >15 | # # # # # # # ## # ## # # ## ## # # # # # | > | # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # | >10 |#############################################################| > |#############################################################| >5 |#############################################################| > |#############################################################| > |-------------------------------------------------------------| > > Breadth of knowledge > >We now have a player that still plays well tactically (see the comprehensive >coverage up to ply 10), but also takes into consideration factors that will >affect the position for a great many moves ahead. If this computer were to play >the human, who would win would be anybody's guess! The human would certainly >have to work hard to avoid tactical errors, which would reduce his chances. > >Comments welcome on whether this is a good representation of ply and knowledge, >on whether you agree with my thoughts as depicted by the graphs, or just about >anything else, cordially welcomed. I disagree because all top programs do extensions and pruning so the tree is never like your first or third picture. I do not think that seeing everything to 10 plies and nothing after it is being good at tactics because there are tactical ideas of more than 10 plies and even more than 20 plies. I see tactics as something that I can prove by a tree and it can be sometimes 40 plies or more. positional ideas are something that I cannot practically prove by a tree becaue the tree that I need is too big. I know that this definition is not clear and if I work more time on a position then something that I defined as positional may become tactical. Uri
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