Author: Vincent Diepeveen
Date: 17:15:46 08/06/00
Go up one level in this thread
On August 06, 2000 at 16:48:16, Andrew Williams wrote: >On August 06, 2000 at 16:36:15, Vincent Diepeveen wrote: > >>Show me an MTD program that uses less nodes a ply as DIEP does. >> > >I've no idea if Diep uses fewer nodes than others. However, even >if it does, would you say this is due purely to the superiority >of PVS over MTD? Surely your evaluation is different to other >programs too? > >The point I want to make is that it's not helpful to Larry (or anyone >anyone else) if you just say "MTD(f) sux! PVS rox!" UNLESS you provide >some rationale for your opinion. DIEP uses hell of a lot nodes more if i use MTD. A pawn in DIEP is 1000 points worth. So correct me if i'm wrong: If this iteration i'm at +0.300 next iteration i'm at +0.600 with PVS, then how many researches do i need with MTD? >Andrew > >PS Your "there are no commercial programs using MTD" argument doesn't >really represent a rationale, in my opinion. > > >>What diep is doing is very simple in search: >> >> PVS (starting with -infinite) >> check extensions >> checks in qsearch >> nullmove R=3 >> no other crap. no pruning. Perhaps at WMCC i prune a bit, >> but that's because against computers playing is different. >> >> Yet i'm missing programs using less nodes a ply with MTD. >> I"m missing *any* deep searching program that uses MTD actually. >> >>On August 06, 2000 at 10:31:58, An >> >> >> >>drew Williams wrote: >> >>>On August 06, 2000 at 09:38:18, Vincent Diepeveen wrote: >>> >>>>On August 05, 2000 at 11:37:01, Larry Griffiths wrote: >>>> >>>>>Which Algorithm is considered the best now-adays. >>>> >>>>Depends upon what kind of program you make. >>>> >>>>If you have an evaluation function that has patterns which all deliver >>>>very small penalties and bonusses, from which the summation also adds up >>>>to a near to material only evaluation, then MTD is an interesting >>>>alternative. >>> >>>PostModernist uses MTD. It would be incorrect to describe its evaluation >>>as being "near to material-only". This opinion (on MTD) is one that Vincent >>>has expounded before, without much in the way of supporting evidence. >>> >>>> >>>>If the evaluation function is either big, using a pawn as being >>>>worth 1000 points instead of 1 point, the eval is huge, or having high scores >>>>for for example king safety and or passers, >>>>then you have only 1 option that outperforms >>>>*anything*, and that's nullwindow search also called principal variation >>>>search which is pretty easy to implement. >>>> >>>>Usually at the start of your program MTD looks interesting, if your >>>>program gets better (more knowledge in eval, less bugs in search and >>>>better move ordering), then PVS usually outperforms anything. >>>> >>> >>>I don't think there is any evidence anywhere that supports Vincent's opinion >>>about MTD. Just stating an opinion does not make it true :-) >>> >>>>My advice is to start with PVS and not look to the rest. >>>> >>>>>NegaScout? MTD? PVS? Others? I am looking to implement one of the best search >>>>>type algorithms in my program. I would like to get it into the 2000 rated range >>>>>as this has been my lifetime goal. Then, maybe install winboard or something so >>>>>it can compete against other programs to get a rating. >>>>>I dont like MTD as it seems to be complex. >>>>> >>>>>Larry. >>> >>>My advice would be to get a straight alpha-beta search working, starting >>>with bounds of -inf..+inf. This won't be terribly competitive, but you >>>can use it as a stable reference when you move on to more sophisticated >>>approaches. When you're happy with your alpha-beta search, try implementing >>>an aspiration-search, which is like alpha-beta except that you start with >>>bounds of score-50 .. score+50, where score is the value returned from the >>>previous iteration. You will need to provide some way of handling the case >>>where the returned score from *this* search falls outside this "window". >>>Once you've got your aspiration search working properly, you'll be in a >>>strong position to decide where you want to go with your program. >>> >>>Above all, have fun with your program! >>> >>>Andrew Williams
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