Stockfish parameters

Released: Mar 1, Wraps the open-source Stockfish chess engine for easy integration into python. View statistics for this project via Libraries.

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stockfish parameters

Latest version Released: Mar 1, Navigation Project description Release history Download files. Project links Homepage. Maintainers zhelyabuzhsky. Project description Project details Release history Download files Project description Stockfish Implements an easy-to-use Stockfish class to integrates the Stockfish chess engine with Python. Please see License File for more information.

Project details Project links Homepage. Release history Release notifications This version. Download files Download the file for your platform. Files for stockfish, version 3.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Stockfish is a free, powerful UCI chess engine derived from Glaurung 2.

Read the documentation for your GUI of choice for information about how to use Stockfish with it. By default, contempt is set to prefer the side to move. Set this option to "White" or "Black" to analyse with contempt for that side, or "Off" to disable contempt. The number of CPU threads used for searching a position. For best performance, set this equal to the number of CPU cores available.

Output the N best lines principal variations, PVs when searching. Leave at 1 for best performance. Internally, MultiPV is enabled, and with a certain probability depending on the Skill Level a weaker move will be played. This option overrides Skill Level. Assume a time delay of x ms due to network and GUI overheads.

Contempt Factor

This is useful to avoid losses on time in those cases. Lower values will make Stockfish take less time in games, higher values will make it think longer. Tells the engine to use nodes searched instead of wall time to account for elapsed time. Useful for engine testing.

Multiple directories are to be separated by ";" on Windows and by ":" on Unix-based operating systems. Do not use spaces around the ";" or ":". It is recommended to store. There is no loss in storing the.

It is recommended to verify all md5 checksums of the downloaded tablebase files md5sum -c checksum. Minimum remaining search depth for which a position is probed. Set this option to a higher value to probe less agressively if you experience too much slowdown in terms of nps due to TB probing. Disable to let fifty-move rule draws detected by Syzygy tablebase probes count as wins or losses.At the beginning of December Stockfish 10 was released.

Stockfish has won the last three unofficial world championships for chess engines TCEC and confirmed its dominant position by also winning the chess engine tournament at chess.

Stockfish 10 is about 50 points stronger than Stockfish 9 and points better than Stockfish 8. This parameter is an indication of how much risk a chess engine takes to win by striving for unbalanced positions. In Stockfish 10 the contempt factor is now standard set at 24, in Komodo at 16 and in Houdini at GM Larry Kaufman, developer of Komodo, recommends raising the contempt factor against grand masters to 50 and against masters even further to 75!

Komodo, which is now part of chess. The biggest progress in Komodo The Komodo team, inspired by LeelaZero and AlphaZero, built this technique as a special option into their chess engine and version The rating of Komodo I believe that the Komodo - MCTS engine provides an interesting second opinion to the traditional alpha-beta search based engines. The team has repeated the match with Stockfish 8 and addressed the heavily criticized uneven hardware conditions of the previous match.

This time AlphaZero won Considering that Stockfish 10 is about points stronger than Stockfish 8 ,this margin is not overly impressive. Of course it remains a unique and very interesting approach and furthermore it is mentioned in the Science article that AlphaZero also beat Stockfish 9 with 'large margins'. You can download the article the Science article here: AlphaZero Science Article The programmer-collective of Leela, who is building an engine based on the AlphaZero concept of self learning, has received a lot of new information through the latest publication of Google Deepmind and is using that to direct the further development of Leela now: See AlphaZero learnings for Leela.

Finally, two beautiful moments from the match between Alpahzero and Stockfish 8. The first one is a Dutch Leningrader, the second one an ultrasharp Bg5 - Najdorf. NM HanSchut. Updated: Dec 12,PM. You can download the article the Science article here: AlphaZero Science Article The programmer-collective of Leela, who is building an engine based on the AlphaZero concept of self learning, has received a lot of new information through the latest publication of Google Deepmind and is using that to direct the further development of Leela now: See AlphaZero learnings for Leela Finally, two beautiful moments from the match between Alpahzero and Stockfish 8.

Please have a look at AlphaZero's long term piece sacrifice to gain access to White's king. AlphaZero lost a crucial tempo in this Bg5-Najdorf and is now with it's back against the wall. Have a look at AlphaZero's creative defense: Rxa2 followed by All Blogs. Top Bloggers.The following tuning method was used to significantly improve Stockfish's playing strength ELO points.

The method is a practical approach and not mathematically very sound. Because algorithm is very simple, it's very likely already invented a long time ago. No pseudo- or source-code is given, just an idea behind the algorithm. Let's assume that we have a single variable x which we want to tune.

Alpha Zero's "Immortal Zugzwang Game" against Stockfish

Current value for x is We assume that this value is quite good because we already have a strong enginebut not perfect. Next we need to choose delta for x. However delta must not to be too big, because then asymmetries start to play a big role. One just need to use his intuition here. Now we match engines engine 80 and engine [self-play]. If engine wins, we tune x slightly upwards. So in that case new value for x would be.

Next match would be engine Instead of fixing delta, we fixed standard deviation of Gaussian distribution and calculated a random value for delta for each iteration. But again one needs to use his intuition to pick a suitable value for standard deviation. Doing this for only one variable at a time would be a way too slow. Instead we used to tune variables at the same time. As an example let's say that we have three variables:.

We would then calculate a random delta with sign for each of these variables based on Gaussian distribution. Let's say we get. With super-fast games, we usually got some improvement compared to only by hand tuned values. What actually happens with multiple variables is that most important variables shall dominate and they shall approach their optimal values, while less important variables take "a random walk".

In the beginning this increases strength. But after a while important variables stop approaching optimal values and "random walk" takes over and the strength starts to decrease.

So the method doesn't converge and it needs to be stopped at "suitable moment". This is a big problem as well as manual selection of deltas or standard deviation of delta. Most other tuning algorithms start "from scratch". Although we know a value which is very close to optimal, they make no use of it. Instead method described in here starts from that "very good" value and attempts to improve it slightly. Variable selection is extremely important. So in tuning we end up doing only random walk and the strength of the program only goes slightly downhill.

But instead using ampli-bias knobs for tables proved to be very successful approach for us. Each chess engine is full of different kind of tables and if we can give even "a slight push" for each of these tables, it'll result in considerable increase in the end. From Chessprogramming wiki.Bench: It is our pleasure to release Stockfish 11 to our fans and supporters.

This makes Stockfish the strongest chess engine running on your smartphone or normal desktop PC, and we estimate that on a modern four cores CPU, Stockfish 11 could give time odds to the human chess champion having classical time control, and be on par with him.

More specific data, including nice cumulative curves for the progression of Stockfish strength over the last seven years, can be found on [our progression page][1], at [Stefan Pohl site][2] or at [NextChessMove][3].

In October Stockfish has regained its crown in the TCEC competition, beating in the superfinal of season 16 an evolution of the neural-network engine Leela that had won the previous season. This clash of style between an alpha-beta and an neural-network engine produced spectacular chess as always, with Stockfish [emerging victorious this time][0].

Our testing framework [Fishtest][5] has also seen its share of improvements to continue propelling Stockfish forward. Along with a lot of small enhancements, Fishtest has switched to new SPRT bounds to increase the chance of catching Elo gainers, along with a new testing book and the use of pentanomial statistics to be more resource-efficient. Overall the Stockfish project is an example of open-source at its best, as its buzzing community of programmers sharing ideas and daily reviewing their colleagues' patches proves to be an ideal form to develop innovative ideas for chess programming, while the mathematical accuracy of the testing framework allows us an unparalleled level of quality control for each patch we put in the engine.

If you wish, you too can help our ongoing efforts to keep improving it, just [get involved][6] :- Stockfish is also special in that every chess fan, even if not a programmer, [can easily help][7] the team to improve the engine by connecting their PC to Fishtest and let it play some games in the background to test new patches.

Thanks Guo! Further work in tweaking constants can always be done - numbers are guessed "by hand" and are not results of some tuning, maybe there is some more Elo to squeeze from this part of code. The idea is that the next iteration will generally take about the same amount of time as has already been used in total. Romstad, M. Costalba, J. Kiiski, G. I picked two large terms early futility pruning and singular extensionso with small relative error.

It turns out it is actually quite interesting see figure 1. Contrary to my expectation, the Elo gain for early futility pruning is pretty time control sensitive, while singular extension gain is not.

Figure 1: TC dependence of two search terms! It seems like a nice example of how connected terms in search really are, i.

stockfish parameters

No functional change. I believe master is a bit convoluted here and propose this version for clarity. This is part of a problem which is commonly referred to as the Graph History Interaction GHIand is difficult to solve in computer chess because storing the moves counter in the hash-table loses Elo in general. The idea is that in such cases values from previous searches, with a much lower move count, become less and less reliable.

More precisely, the heuristic we use in this patch is that we don't take the transposition table cutoff when we have reached a moves limit, but let the search continue doing its job. There is a possible slowdown involved, but it will also help to find either a draw when it thought to be losing, or a way to avoid the draw by move rule. This heuristics probably will not fix all possible cases, but seems to be working reasonably well in practice while not losing too much Elo.

The latter is used in lieu of ordinal categorical modelling. Idea is somewhat similar to outflanking - endgames are hard to win if each king is on it side of the board. So this adds extra bonus for one of kings crossing the middle line. This adds a condition for quiet futility pruning: history total has to be low. Instead, compare with the static eval of 2 moves before. Nice idea by Alain Savard!

Indeed, because the tuned Kc2 and Kf2 values were quite different, it was a good idea to try something more neutral. It was tried a couple of times by now but now it passed. Performance is not enormously good but this patch makes a lot of sence - blockers for king can't really move until king moves in most cases so logic behind it is the same as behind excluding king square from mobility area.

Seems like PvNode check in condition of last capture extension is not needed.For most chess positions, computers cannot look ahead to all final possible positions.

For example, in standard chess terminology, one move consists of a turn by each player; therefore a ply in chess is a half-move.

stockfish 3.7.0

The algorithm that evaluates final board positions is the evaluation function, which differs between different chess engines. While the human method of analyzing alternatives seems to involve selecting a few promising lines of play and exploring them, computers are necessarily exhaustive rather than selective, so refinement techniques have been and continue to be developed.

Stockfish uses a complicated set of analysis functions depending on what material is on the board. By applying small changes and refinements i.

stockfish parameters

Stockfish continues to expand as additions and tweaks are added by various developers. Science Usage The user requirements for Stockfish are mercifully little, for it is an open-source cross-platform engine.

The only pre-requisites to using Stockfish are downloading the source code from their website. Goals The ultimate goal of Stockfish is to unite the chess-program-developer community, and continue to a build stronger, faster chess engine.

This includes, among other things, a goal to improve the effectiveness of search evaluations. By creating an efficient board representation, this provides a better toolbox to guide the chess tree search, which improves the AI.

These enhancements come from the fact that if you restrict the window of scores that are interesting, you can achieve more cutoffs. The latest technique of move ordering is applied outside of the search, using iterative deepening boosted by a transposition table. Results Stockfish has clearly demonstrated that simple, brute-force approaches should not be quickly discarded. Additionally, iterative techniques, in particular, ideas developed for alpha-beta search and iterative deepening, are applicable to other search domains.

Stockfish has clearly demonstrated the inadequacy of conventional AI techniques for realtime computation. Stockfish does not use AI languages or knowledge representation methods, for these conventions are too slow for a real-time, high performance application. I have found that Stockfish represents the state of the chessboard using bitboards, it evaluates the static board positions using a categorical and statistical representation and uses an advanced alpha-beta search algorithm.

In order to not analyze the same position several times, a transposition table is used. This is essentially memorization applied to the search function.

Step 2. Check for aborted search and immediate draw. Enforce node limit here. This only works with 1 search thread, as of Stockfish 2. Step 3. Mate distance pruning. Same logic but with reversed signs applies also in the opposite condition of being mated instead of giving mate, in this case return a fail-high score.

Step 4. Transposition table lookup. We use a different position key in case of an excluded move. Step 7. Static null move pruning is omitted in PV nodes. Step 9. If we have a very good capture and a reduced search returns a value much above beta, we can almost safely prune the previous move. Step Loop through moves.Hey, I'm new to this site, so hopefully this is the right place to post this.

I tried googling, and while I found some info hash is the amount of memory being used,but I don't know what's the right amount; I read that threads should be set to how many cores you havemost of it was over my head. I'll make a stab at it, although I'm not an expert at this. To begin with, here are a few links that might fill in some gaps:. I typically use MB.

I could probably set it quite a bit higher, but this setting seems to work OK for me. Some people use gigabytes of hash. The only thing to watch out for is that you don't set it so high that you run out of free RAM when you're running the engine and any background applications.

If you're playing against the engine and you want the engine to think on your own time, then use "ponder on". It's apparently only an issue if you're running background processes that might prevent the engine from using all of its needed resources. I'd think as long as you don't max out the CPU usage, you won't have to worry about this setting and you can just leave it on the default setting. Threads - One thread is the default, and it would be safe to use that setting.

If you need better performance, you can increase the number of threads. However, using too many threads might max out your CPU usage, and some PCs could overheat if not cooled sufficiently. Clear hash - Self explanatory. Click the button to manually clear the hash, if that's what you want to do.

I usually only use this if I'm running manual analysis on a position and need to restart the engine with no previous lines in the hash memory. MultiPV - This setting specifies how many principal variations are to be displayed. Using greater than one will slow down the engine's calculations somewhat. It's a useful setting if you need to see more than just the best line. SyzygyPath - If you downloaded the Syzygy tablebases, this specifies the path to those tablebase files. Nov 4, 1.

Nov 4, 2. SyzygyProbeLimit - Ditto, same as above. Move Overhead - Refer to the applicable link above. Slow Mover - Refer to the applicable link above. Log In or Join. Hot Topics. Getting a new chessboard -- looking for suggestions to pieces sound67 19 min ago. Is there a time-cheat?