On a final fixed game number test it failed
to prove better than standard version.
STC 15+0.05
ELO: -0.86 +-1.7 (95%) LOS: 15.8%
Total: 57578 W: 10070 L: 10213 D: 37295
bench: 8802105
If we return from split with a stale value
due to a stop or a cutoff upstream occurred,
then we exit moves loop and save a stale value
in TT before returning search().
This patch, from Joona, fixes this.
bench: 8678654
We can never have bestValue == -VALUE_INFINITE at
the end of move loop because if no legal move exists
we detect it with previous condition on !moveCount,
if a legal move exists we never prune it due to
futility pruning condition:
bestValue > VALUE_MATED_IN_MAX_PLY
So this code never executes, as I have also verified
directly.
Issue reported by Joona.
No functional change.
This is a very discussed patch with many
argumentations pro and against. The fact is
it passed both STC:
LLR: 2.96 (-2.94,2.94) [-1.50,4.50]
Total: 16305 W: 3001 L: 2855 D: 10449
And LTC
LLR: 2.95 (-2.94,2.94) [0.00,6.00]
Total: 34273 W: 5180 L: 4931 D: 24162
Although it is true that a correct test should
include foreign engines, we commit it anyhow so
people can test it out in the wild, under broader
conditions.
bench: 7384368
This is more consistent with what other engines are doing.
Often people thinks that SF's scores are overblown. In the
end, it just boils down to the arbitrary way of rescaling them.
No functional change.
Tested directly at LTC because previous long
test series on this topic shows it is TC dependant.
Tested with no-regression mode because gets rid of
an ugly and ad-hoc rule.
Test at LTC:
LLR: 2.95 (-2.94,2.94) [-3.00,1.00]
Total: 67918 W: 10590 L: 10541 D: 46787
bench: 7926803
Thanks to std::bitset we can easily increase
the limit of active threads above 64.
Thanks to Lucas Braesch for pointing at the
correct solution of using std::bitset.
No functional change.
Split delta value in aspiration window so that when
search depth is less than 24 a smaller delta value
is used. The idea is that the search is likely to
be more accurate at lower depths and so we can exclude
more possibilities, 25% to be exact.
Passed STC
LLR: 2.96 (-2.94, 2.94) [-1.50, 4.50]
Total: 20430 W: 3775 L: 3618 D: 13037
And LTC
LLR: 2.96 (-2.94, 2.94) [0.00, 6.00]
Total: 5032 W: 839 L: 715 D: 3478
Bench: 7451319
It has been obsoleted out already some time ago
and currently there is no point in changing eval
score according to if we are in game or analyzing.
So retire the option.
No functional change.
Try to avoid repetition draws at early midgame,
this should give an edge against weaker opponents
and reduce draw rate.
Tested for regressions with SPRT[-3, 1] and
passed both short TC
LLR: 2.95 (-2.94,2.94) [-3.00,1.00]
Total: 68498 W: 12928 L: 12891 D: 42679
And long TC
LLR: 2.96 (-2.94,2.94) [-3.00,1.00]
Total: 40212 W: 6386 L: 6295 D: 27531
bench: 7990513
When running the following position:
8/kPp5/2P3p1/p1P1p1P1/2PpPp2/3p1p2/3P1P2/5K2 w - - 0 1
An assert is raised at depth 92:
assert(-VALUE_INFINITE <= alpha && alpha < beta && beta <= VALUE_INFINITE);
This is because it happens that beta = 29832,
so rbeta = 30032 that is > VALUE_INFINITE
Bug spotted and analyzed by Uri, fix suggested by Joerg.
Other fixes where possible but this one is pointed
exactly at the source of the bug, so it is the best
from a code documentation point of view.
bench: 8430785
Actually MultiCut is too different from current scheme.
Note that neither ProbCut is exactly what we do because
we try just a handful of captures instead of all moves,
nevertheless it seems more in line with what we do.
Suggested by Joona.
No functional change.
Makes more sense than returning a draw score. Tested
with reduced MAX_PLY = 30 and passed both short TC
LLR: 2.95 (-2.94,2.94) [-1.50,4.50]
Total: 17434 W: 3345 L: 3194 D: 10895
And long TC
LLR: 2.97 (-2.94,2.94) [0.00,6.00]
Total: 2610 W: 488 L: 373 D: 1749
With current limit of MAX_PLY = 100 the patch should not
introduce any measurable change, nevertheless is the correct
approach.
Idea of returning eval is from Michel Van den Bergh.
bench: 8430785
Although does not change ELO level, it seems
verification is useful in many zugzwang positions
as reported by many sources.
So revert this simplification.
bench: 8430785
Tested with SPRT in simplification mode [-4.00,0.00],
this ensures that the patch is (very probably) not
a regression.
Passed both short TC
LLR: 2.95 (-2.94,2.94) [-4.00,0.00]
Total: 27543 W: 4278 L: 4209 D: 19056
And long TC
LLR: 2.95 (-2.94,2.94) [-4.00,0.00]
Total: 39483 W: 7325 L: 7305 D: 24853
bench: 8347121
Hopefully this patch makes the code more:
* Self-documenting: Null search is always a zero window search,
because it is testing for a fail high. It should never be done
on a full window! The current code only works because we don't
do it at PV nodes, and therefore (alpha, beta) = (beta-1, beta):
that's the kind of "clever" trick we should avoid.
* Idiot-proof: If we want to enable null search at PV nodes, all we
need to do now is comment out the !PvNode condition. It's that simple!
In theory, null search should not be done at PV nodes, because PV nodes
should never fail high. But in practice, they DO fail high, because of
aspiration windows, and search inconsistencies, for example. So it makes
sense to keep that flexibility in the code.
No functional change.
Use ralpha instead of rbeta
* rbeta is confusing people. It took THREE attempts to code razoring
at PV nodes correctly in a recent test, because of the rbeta trick.
Unnecessary tricks should be avoided.
* The more correct and self-documenting way of doing this, is to say
that we use a zero window around alpha-margin, not beta-margin.
The fact that, because we only do it at PV nodes, alpha happens to be
beta-1 and that the current stuff with rbeta works, may be correct,
but is confusing.
Remove the misleading and partially erroneous comment about returning
v + margin:
* comments should explain what the code does, not what it could have done.
* this comment is partially wrong in saying that v+margin is "logical",
and that it is "surprising" that is doesn't work.
From a theoretical perspective, at least 3 ways of doing this are equally
defendable:
1/ fail hard: return alpha: The most conservative. We bet that the search
will fail low, but we don't know by how much and don't want to take risks.
2/ aggressive fail soft: return v (what the current code does). This
corresponds to normal fail soft, with the added assumption that we don't
care about the reduction effect (see below point 3/)
3/ conservative fail soft: return v + margin. If the reduced search (qsearch)
gives us a score <= v, we bet that the non reduced search will give us a
score <= v + margin.
* Saying that 2/ is "logical" implies that 1/ and 3/ are not, which is
arguably wrong. Besides, experimental results tell us that 2/ beats 3/,
and that's not something we can argue against: experimental results are
the only trusted metric.
* Also, with the benefit of hindsight, I don't think the fact that 2/ is
better than 3/ is surprising at all. The point is that it is YOUR turn to
move, and you are assuming that by NOT playing (and letting the opponent
capture your hanging pieces in QS) you cannot generally GAIN razor_margin(depth).
No functional change.
Depth is already dependent on the actual value
of ONE_PLY, in particular can be expressed like:
Depth = n * ONE_PLY
And because formula is used to calculate R that is
also dependent on the value of ONE_PLY and can be
expressed like:
R = x * ONE_PLY
We don't want to divide depth by a 'ply' value but
directly by an integer number.
Spotted by sf-x
No functional change.
Instead of a fixed reduction of ONE_PLY, now
Null move dynamic reduction based on value can
grow larger in case we are above beta of a value
much higher then PawnValueMg.
Note that now an eval returning VALUE_KNOWN_WIN
makes null search to drop in qsearch.
Passed both short TC:
LLR: 2.95 (-2.94,2.94) [-1.50,4.50]
Total: 26141 W: 4871 L: 4699 D: 16571
And long TC:
LLR: 2.97 (-2.94,2.94) [0.00,6.00]
Total: 33695 W: 5309 L: 5056 D: 23330
bench: 7356053
When we have a fail-high of a quiet move, store it in
a Followupmoves table indexed by the previous move of
the same color (instead of immediate previous move as
is in countermoves case).
Then use this table for quiet moves ordering in the same
way we are already doing with countermoves.
These followup moves will be tried just after countermoves
and before remaining quiet moves.
Passed both short TC
LLR: 2.95 (-2.94,2.94) [-1.50,4.50]
Total: 10350 W: 1998 L: 1866 D: 6486
And long TC
LLR: 2.95 (-2.94,2.94) [0.00,6.00]
Total: 14066 W: 2303 L: 2137 D: 9626
bench: 7205153
While editing original Uri's messy patch
I have incorrectly simplified the logic
condition. Here is the correct original
version, as it was tested.
bench: 8502826
Remove the easy move code and add the condition to
play instantly if only one legal move is available.
Verified there is no regression at 60+0.05
ELO: 0.17 +-1.9 (95%) LOS: 57.0%
Total: 40000 W: 6397 L: 6377 D: 27226
bench: 8502826
If we are still at first move, without a fail-low and
current iteration is taking too long to complete then
stop the search.
Passed short TC:
LLR: 2.97 (-2.94,2.94) [-1.50,4.50]
Total: 26030 W: 4959 L: 4785 D: 16286
Long TC:
LLR: 2.95 (-2.94,2.94) [0.00,6.00]
Total: 18019 W: 2936 L: 2752 D: 12331
And performed well at 40/30
ELO: 4.33 +-2.8 (95%) LOS: 99.9%
Total: 20000 W: 3480 L: 3231 D: 13289
bench: 8502826
A great simplification that shows no regression
and it seems even a bit scalable.
Tested with fixed number of games:
Short TC
ELO: 0.60 +-2.1 (95%) LOS: 71.1%
Total: 39554 W: 7477 L: 7409 D: 24668
Long TC
ELO: 2.97 +-2.0 (95%) LOS: 99.8%
Total: 36424 W: 5894 L: 5583 D: 24947
bench: 8184352
Change updating rule after a TT hit to match
the same one at the end of the search.
Small change in functionality, but we want to
have uniform rules in the code.
bench: 7767864
We already update killers so it is natural to extend to
history and counter move too.
Passed both short TC
LLR: 2.95 (-2.94,2.94) [-1.50,4.50]
Total: 52690 W: 9955 L: 9712 D: 33023
And long TC
LLR: 2.96 (-2.94,2.94) [0.00,6.00]
Total: 5555 W: 935 L: 808 D: 3812
bench: 7876473
After a fail high in LMR, if reduction is very high do
a research at lower depth before teh full depth one.
Chances are that the re-search will fail low and the
full depth one is skipped.
Passed both short TC:
LLR: 2.95 (-2.94,2.94) [-1.50,4.50]
Total: 11363 W: 2204 L: 2069 D: 7090
And long TC:
LLR: 2.95 (-2.94,2.94) [0.00,6.00]
Total: 7292 W: 1195 L: 1061 D: 5036
bench: 7869223
Since all ENPASSANT moves are now considered dangerous, this
change of order should give a slight speedup.
Also simplify futilityValue formula.
No functional change.
