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Subject: Re: Omid didn't read Heinz article at all!

Author: Omid David Tabibi

Date: 21:48:41 12/20/02

Go up one level in this thread

I didn't compare to Adaptive Null-Move Pruning, and neither drew any conclusions
as to which one is better. It is up to you -- the programmer -- to test both
Verified and Adaptive schemes, and find out which one performs better in your
program. Although as I've said before, I believe that starting from a certain
depth (threshold), Verified Null-Move Pruning will always be superior to the
Adaptive one (since unlike the Adaptive version, Verified's backbone is pure
R=3). This depth threshold varies from one program to another (one programmer
reported the threshold to be 12 plies in his programs, i.e., Verified Null-Move
Pruning performed better only after depth 12). So you have to test this
algorithm in longer time controls and deeper searches.

BTW, you quoted from Section 4 of Heinz' paper. In Section 3, Heinz describes
that R=2 is superior to R=2. Quote from second paragraph of Section 3, Adaptive
Null-Move Pruning, 1999:

"Regarding the depth reduction of null-move searches, the accepted standard
recommended the usage of a constant depth-reduction factor R=2. Independent
studies by several researchers confirmed that recursive null-move pruning with
R=2 behaved markedly better than both the too conservative R=1 and the too
aggressive R=3."

On December 20, 2002 at 17:18:54, Vincent Diepeveen wrote:

>On December 19, 2002 at 12:13:46, Omid David Tabibi wrote:
>I assume Heinz doesn't mind i quote his summary and part of his
>conclusion that is relevant to this issue. If so then
>apologies for Heinz for quoting his research litterary.
>Page 123 september 1999 issue.
>"..  To this end, we extend standard null-move pruning by a variable
>depth reduction and introduce what we call adaptive null-move pruning.
>Quantitative experiments with our chess program Darkthought show that
>adaptive null-move adds a new member to the collection of successful
>forward-pruning techniques in computer chess. It preserves the tactical
>strength of Darkthought while reducing its search effort by 10 to 30%
>on average in comparision with standard nullmove pruning at search
>depths 8 to 12 plies. ..."
>So where is your compare to adaptive nullmove? Heinz concludes
>adaptive nullmove is better for his program that is doing a lot of
>forward pruning at the leafs and no checks in the qsearch i assume.
>Then the conclusion of Heinz. The *entire* conclusion. All sentences.
>Nothing is left out:
>"Our brief historical overview of search paradigms in computer chess argued that
>the pure brute-force searchers lost their traditional dominance of the field
>around 1990. Since then selective forward-pruning techniques have again taken
>the lead, guided by the spectacular achievemens of the null move in
>microcomputer practice. Today, nearly all the world-class chess programs apply
>various clever forward-pruning schemes with good success.
>This article adds adaptive null-move pruning to the collection of succesful
>forward pruning methods in computer chess. Extensive experiments show that
>adaptive null-move pruning preserves the tactical strength of our sophisticated
>and tournament-proven chess program Darkthought while reducing its search effort
>by 10 to 30% on average in comparision with standard R=2 null-move pruning at
>search depths of 8 to 12 plies. Furthermore, adaptive null-move pruning and its
>savings in search effort scale equally well with search depth as those of the
>extremely risky R=3 scheme. Overall, our fine-tuned implementation of adaptive
>null-move pruning in darkthought combines the merits of both standard R=2 and
>aggressive R=3 nullmove pruning in a convincing way. In view of its tactical
>safety and the neatly reduced search effort, we deem it quite remarkable that
>adaptive null-move pruning is very easy to implement.
>During the course of our research on adaptive null-move pruning we also
>discovered an important distinction between dynamic and static forward-pruning
>schemes regarding their preferred degrees of selectivity in different parts of
>the full-width search. While static forward pruning generally thrives on low
>selectivity in the upper parts and the increased selectivity in the lower ones,
>dynamic forward pruning excels with the exact opposite. Hence, dynamic forward
>pruning rather prefers high remaining search depths for the application of
>aggressive selectivity. Their specific preferences allow both styles of forward
>pruning to sustain the desired degrees of tactical safety."
>First of all:
>  - Ernst A. Heinz says nowhere that R=2 is better than R=3. He just
>    side comments R=3 is risky because it is aggressively pruning.
>  - Ernst A. Heinz DOES conclude that adaptive nullmove pruning is better
>    for darkthought at the search depths 8-12.
>Now where todays software programs are above that 12 and tend to go more
>to R=3 especially when having checks in qsearch, still the vaste majority
>of software programs is having adaptive nullmove and many concluded that it
>was best for them.
>You researched depths 9 and 10 and didn't compare with adaptive nullmove
>*despite* saying you know the article.
>Explain that please.
>>On December 19, 2002 at 09:53:21, Vincent Diepeveen wrote:
>>>Your own data shows that R=3 produces a far smaller tree
>>>and you didn't compare time to solution in any of the
>>>tests performed.
>>>So you cannot claim anything about verified nullmove.
>>>The tactics you solve easily with checks in qsearch.
>>>Heinz concluded a combination of R=3 + R=2 is better,
>>>*not* that R=2 is better than R=3 in itself.
>>Vincent, it seems that you have neither read Heinz' article. Read Section 3 of
>>Adaptive Null-Move Pruning, Heinz 1999.
>>>On December 18, 2002 at 22:50:56, Omid David Tabibi wrote:
>>>>On December 18, 2002 at 22:36:38, Andrew Dados wrote:
>>>>>On December 18, 2002 at 22:12:43, Omid David Tabibi wrote:
>>>>>>On December 18, 2002 at 22:08:39, Andrew Dados wrote:
>>>>>>>On December 18, 2002 at 22:00:08, Omid David Tabibi wrote:
>>>>>>>>On December 18, 2002 at 21:48:03, Andrew Dados wrote:
>>>>>>>>>On December 18, 2002 at 18:56:21, Omid David Tabibi wrote:
>>>>>>>>>>On December 18, 2002 at 18:12:22, Bruce Moreland wrote:
>>>>>>>>>>>On December 18, 2002 at 16:59:10, Omid David Tabibi wrote:
>>>>>>>>>>>>You take two numbers and draw a very general conclusion. Look at other tables
>>>>>>>>>>>>and depths, which show a more significant superiority of std R=2 over std R=3.
>>>>>>>>>>>>Look at Tables 2 and 6. Vrfd R=3 solved almost the same number of positions as
>>>>>>>>>>>>std R=1 !!! Does it leave any room for doubt as for vrfd R=3's superiority over
>>>>>>>>>>>>std R=3 ?
>>>>>>>>>>>I don't see anything that shows demonstrated superiority of R=2 over R=3.  You
>>>>>>>>>>>say to look at table 2 -- so do I.  It shows that R=2 gets one more correct
>>>>>>>>>>>through ply 10, but takes over twice as long to do it.  I suggest that if R=3
>>>>>>>>>>>were allowed to continue until R=2 is finished, that it would have found
>>>>>>>>>>>significantly more than 1 solution in the mean time.
>>>>>>>>>>>Table 6 has no node counts, so I don't know how much faster R=3 is than R=2.  It
>>>>>>>>>>>gets 286 as opposed to 292.  Fine.  How much less time did it take to get it?
>>>>>>>>>>>Maybe VR=3 is better than R=3.  The paper should allow me to draw this
>>>>>>>>>>>A reason that I bring up the comparison between R=3 and R=2, is if you are
>>>>>>>>>>>proving that R=3 is better than R=2, and you don't think that R=3 is better than
>>>>>>>>>>>R=2, then maybe your other results are flawed.
>>>>>>>>>>>You are writing a paper on some aspect of biological science, and your data is
>>>>>>>>>>>suddenly implying that evolution doesn't take place.  Doesn't *that* seem worth
>>>>>>>>>>>Either you are on the verge of a serious breakthrough, or your testing process
>>>>>>>>>>>is wrong.  You need to figure out which.
>>>>>>>>>>Apparently we are not looking at the data from the same perspective. As I told
>>>>>>>>>>you before, I conducted self-play matches, and their results showed that std R=2
>>>>>>>>>>is superior to std R=3. Although I still think that this finding is not worth
>>>>>>>>>>publishing, as it is an already known fact.
>>>>>>>>>>I understand your criticism of the fixed depth method, which is the standard
>>>>>>>>>>scientific comparison in computer chess. But I'm afraid your case against fixed
>>>>>>>>>>depth is not strong enough to convince the whole computer chess research
>>>>>>>>>>community to opt for fixed time comparisons instead.
>>>>>>>>>>Mentioning some fixed time experiments in a footnote or appendix could have been
>>>>>>>>>>interesting; but even without them, my experiments took more than 6 months
>>>>>>>>>>24h/d, 7d/w.
>>>>>>>>>>If you have a specific experiment in mind, I would be glad to conduct whenever I
>>>>>>>>>>get the time, but besides that, I would like the implemented algorithm in your
>>>>>>>>>>program to speak for its own.
>>>>>>>>>>In our discussion today, I didn't get into details and kept my replies short,
>>>>>>>>>>because none of your points were new, and I have already discussed all these in
>>>>>>>>>>detail a few weeks ago. I'm sure anyone who followed those discussions could
>>>>>>>>>>have answered all your questions.
>>>>>>>>>>Based on the programmers' feedbacks I additionally posted several implementation
>>>>>>>>>>suggestions for the various variants of this algorithm, which I'm sure you'll
>>>>>>>>>>find helpful.
>>>>>>>>>>Now you will have to excuse me for not being able to continue the discussion,
>>>>>>>>>>for I am up to my ears busy working on another paper (on Blockage Detection)
>>>>>>>>>>which I hope to be ready soon.
>>>>>>>>>Please, refrain...
>>>>>>>>Sorry Andrew, I didn't understand what you meant.
>>>>>>>I meant refrain from producing tons of 'scientific' papers with wrong or
>>>>>>>unjustified conclusions. Instead of vr=3 and 80% more nodes overhead simply
>>>>>>>implement checks in q-search and get more tests right with 10-15% overhead.
>>>>>>>Btw.. try self-playing vr=3 versus r=2 or 3 + checks in qsearch :)
>>>>>>And why do you think that vrfd R=3 won't be superior to std R=2 if they both use
>>>>>>checks in q-search?
>>>>>Then what do you need to verify? Biggest problem with null move is when it drops
>>>>>you right into qsearch and you don't do checks after that.
>>>>Based on the feedback I've received from other programmers, verified null-move
>>>>pruning usually yielded a considerable tactical improvement even in programs
>>>>using checks in q-search and other extensions.
>>>>The idea of verified null-move pruning will work fine on almost every program;
>>>>on some after 10 minutes of implementation, on others after several days of fine
>>>>tuning. The main issue is to find the suitable reduction value after a fail-high
>>>>report. In my experiments I used a reduction of 1 after a fail-high report, but
>>>>programs with more sophisticated extensions might achieve good results with a
>>>>value of 2; or even an adaptive value.
>>>>After a certain depth (a threshold), vrfd R=3 will always create a smaller tree
>>>>than std R=2. For some programs that threshold might be higher (one programmer
>>>>reported the depth 12 to be the threshold in his program!), so the experiments
>>>>should be conducted on higher depths. It is quite natural that for example until
>>>>depth 10, vrfd R=3 constructs a larger tree than std R=2, and starting from
>>>>depth 11, the tree will be smaller. The key issue is to find the threshold.
>>>>>Another (much smaller) problem is when a piece is trapped. Here you may try
>>>>>improving on verification search: When null fails low (I know, you do
>>>>>verification when null fails high) and is refuted with direct capture: either
>>>>>suppress null move in subtrees, or supress it only when you try evasions of
>>>>>piece en-prise. Or extend. Lots of options, most of us tried those some time
>>>>>ago. This is all old stuff....
>>>>>But I don't think bloating tree by 80% to get null problems right is a good
>>>>80% ?!?
>>>>>>>>BTW, are you still working on Rookie?!
>>>>>>>No, I stopped like 1 year ago.

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