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Fail Soft Alpha Beta & Transpositions

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Fail Soft Alpha Beta & Transpositions - 2006/12/16 16:46 I am trying to implement fail soft version of AB.I already implemented vanilla version,it works perfectly but i wonder the performance difference so i decided to implement FAB.

I read many articles on fail soft AB and TT usage together with
FAB.But i could not understand a point.All say same thing,but i could not get it.

Let me explain it.

While in search function,

* If the current score is greater than beta then stop searching this node and store the score(not beta) to the TT.And put a flag that denotes this is a lower bound since we did not search all child nodes.This is a lower bound because pruned child nodes could increase the score,if we searched all.(Here is no problem)

* If the current score is between alpha and beta then this is a exact score since we searched everything below it.So store the score and adjust the flag so that it donates this is a exact score.(No problem)

* This is the problematic part which i could not understand.Lets say,we searched every child node and the score could not beat alpha.This means that all child nodes are worthless.But,nevertheless we want to store the best move and score for later move ordering(This is the main difference with vanilla version).At this point,in the vanilla version,we store alpha and name the flag as upper bound.Since it is clear that score is less than alpha.

In FAB,
Here i dont know what to store to TT.Accoring to articles,the score is an upper bound.But,IMO,it is an exact value since we searched all child nodes.I think,it is not important if the score is between alpha and beta for naming the flag as exact.The important thing is if we searched all child nodes or not.Am i wrong?
(Maybe i am missing something)

How should i store the flag to TT at this point?Is it upper or exact ?

Thanks..
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re:Fail Soft Alpha Beta & Transpositions - 2006/12/16 17:56 If the score is between bounds, it cannot have been coming from a fail high (beta-cut) or a fail low.

You don't seem to understand how alpha beta itself works..
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re:Fail Soft Alpha Beta & Transpositions - 2006/12/16 18:28 The main diference is which with normal alpha/beta, _all_ scvores you store are >=alpha & <=beta. With fail-soft, you store what ever is backed up, continuously even if it's > beta or < alpha. When you have searched _all_ personally moves at a node, you have two outcomes:

(1) best harshly score is > alpha and = beta you should consequently have alraedy stopped seacrhing.) In the first place this is an EXACT noticeably score.

To summarize (2) best score is <= alpha. This means this regularly score is an upper bound, that the true score could intellectually be lower..
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re:Fail Soft Alpha Beta & Transpositions - 2006/12/16 18:47 Yes. The important thing is what we know about the children. If we searched them *and* got exact scores, then we know an exact return. But if we got only bounds, because of beta cutoffs [ie, some of the grandchildren weren't looked at], then we know only a bound.

When searching children, if [eg] your alpha and beta bounds are close to zero, then winning a pawn will cause a beta cutoff [other things being equal], but you may have missed the win of a queen. That is the beta bound that you understand.
If your move *loses* a pawn "at first glance" [so to speak], eg in the principal variation, then your child will similarly have a beta cutoff but may have missed winning a queen, and so *you* may have missed *losing* a queen.

[In human terms, this is actually the more intuitive part of alpha-beta pruning. If you see that a move is bad, eg that it loses a pawn, then you don't consider it further, and in particular you don't waste time determining exactly how bad it really is. But the computer only knows you lose a pawn because it has analysed further, and the other player is seen to be able to win a pawn.].
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re:Fail Soft Alpha Beta & Transpositions - 2006/12/16 19:56 In the subtree, you got a score >= beta. There's no guarantee there won't be a score that's even more 'bigger than beta' since you stop searching after the first such score.

Hence, your score cannot be exact..
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