You can help make GNU Go the best Go program.
This is a task-list for anyone who is interested in helping with GNU
Go. If you want to work on such a project you should correspond with
us until we reach a common vision of how the feature will work!
A note about copyright. Before any code can be accepted as a part of
the official release of GNU Go, the Free Software Foundation will want
you to sign a copyright disclaimer. Of course you can work on a forked
version without signing such a disclaimer. If you want your changes to
the program to be incorporated into the version we distribute we need
such a disclaimer. Please contact the GNU Go maintainers, Daniel Bump
(bump@sporadic.stanford.edu) and Gunnar Farneback
(gunnar@lysator.liu.se), to get more information and the papers to
Below is a list of things YOU could work on. We are already working on
some of these tasks, but don't let that stop you. Please contact us or
the person assigned to task for further discussion.
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* If you can, send us bug FIXES as well as bug reports. If you see
some bad behavior, figure out what causes it, and what to do about
fixing it. And send us a patch! If you find an interesting bug and
cannot tell us how to fix it, we would be happy to have you tell us
about it anyway. Send us the sgf file (if possible) and attach
other relevant information, such as the GNU Go version number. In
cases of assertion failures and segmentation faults we probably
want to know what operating system and compiler you were using, in
order to determine if the problem is platform dependent.
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These issues are of tactical nature, i.e. they concern some specific
feature or the infrastructure of the engine. Some of these are quiet
small, maybe doable in a day for an experienced GNU Go programmer.
They might also be useful project to start with for a new project
member. Some of them are bigger and demand a deeper knowledge of the
engine internals. The issues are presented here in an approximate
order of perceived difficulty.
* Add more checks in patterns/mkpat.c testing whether the main diagram and
the constraint diagram are consistent.
* Break out handling of movelists into its own file and generalize it.
This is started in 3.1.16. Move lists are used, among other places,
in worms.c where it is used to store moves that capture, save,
threaten to capture and threaten to save the worm.
* Implement move lists storing important moves for dragons and eyes
in the same way as it is used for worms. Half eyes are already
halfway done. The moves are stored, but not the attack and defend
codes (LOSE, KO_A, KO_B and WIN).
* Make the cache not waste storage on 64 bit systems.
* The dragon data is split into two arrays, dragon[] and dragon2[].
The dragon2 array only have one entry per dragon, in contrast to
the dragon array where all the data is stored once for every
intersection of the board. Complete the conversion of eye_data,
half_eye_data, worm and dragon to use the same structure as the
* Support for ko in eyes.db and optics.c.
* Integrate the time handling code in play_gtp.c with the autolevel
code in clock.c. Alternatively, replace them both with something
better. Basing it on some solid system identification theory and/or
control theory wouldn't hurt.
* Write a script which plays through the joseki databases and checks
that the engine really generates a joseki move for all positions in
the databases. This would also be interesting to run with the
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These issues are strategic in nature. They will help us to improve the
playing strength of the program and/or enhance certain aspects of it.
* Extend the regression test suites.
See the texinfo manual in the doc directory for a description of
how to do this. In particular it would be useful with test suites
for common life and death problems. Currently second line groups, L
groups and the tripod shape are reasonably well covered, but there
is for example almost nothing on comb formations, carpenter's
square, and so on. Other areas where test suites would be most
welcome are fuseki, tesuji, and endgame.
* Tuning the pattern databases. These are under constant revision. Tuning
them is a sort of art. It is not necessary to do any programming to do
this since most of the patterns do not require helpers. We would like it if
a few more Dan level players would learn this skill.
* Extend and tune the Joseki database. It might be very useful to implement
a semi-automatic way of doing this. The current method based on sgf files
becomes difficult with existing tools.
* The semeai module is still in need of improvement. (This is underway.)
* GNU Go does not have a move generator that tries explicitly to build
moyos, or reduce/invade opponent's moyos. Such a move generator could
be built using the same type of code that is used in the owl life and
death reader, or the connection reader mentioned in point 5 above.
* A much improved combination module. The combination module of
today only finds combinations of threats to capture enemy groups.
A more useful combination module would e.g. find combinations of
threats to capture a group or enter opponent territory. It would
also be strong enough to find combinations of strategic moves and
more indirect threats (a threat to a threat). Possibly it could
combine threats in AND-OR trees (DAGs?) that could be searched
using ordinary tree search algorithms. (Revision of combination.c
* Speed up the tactical reading. GNU Go is reasonably accurate when
it comes to tactical reading, but not always very fast. The main
problem is that too many ineffective moves are tested, leading to
strange variations that shouldn't need consideration. To improve
one could refine the move generation heuristics in the reading.
Also, one should implement some more of the standard tree search
optimizations used in alpha-beta readers.
* Improve the heuristics for assessment of the safety of a
group. This might take into account number of eyes / half eyes,
moyo in corners, moyo along the edge, moyo in the center, proximity
to living friendly groups, weak opponent groups etc. It is of
particular interest to be able to accurately determine how a move
affects the safety of all groups on the board.
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These are some ideas that have been floated on the mailing list. Some
of them are down-to-earth, and some are just blue sky ramblings. They
are presented here for inspiration.
A start is being made with GoThic, a goban widget based on the Qt
toolkit. This is linked from the GNU Go development web page on
gnu.org. Other starts have been made based on GTK+, but so far
nothing more than a start has been attempted.
* A graphical pattern editor.
This would make it much easier for non-programmers to improve the
strength of GNU Go. It could also be used as a debugging tool for
the programmers. This project has the GUI as a prerequisite.
The challenge here is not to make a tool which makes it easier to
create patterns but to make it easier to overview and maintain the
* Make the engine thread safe and use multiple CPUs on an SMP
* Making the engine use many machines loosely connected on the
internet or in a cluster.
* Think on the opponent's time.
* A global alpha-beta reader. This would probably be very slow and
could only read 2 or 3 moves ahead. Still it could find fatal
errors and improve the moves that GNU Go makes.
* A strategic module that identifies high-level goals and then gives
these goals to the rest of the engine. It should be able to
identify if we are ahead in territory or thickness, if we should
play safe or if we should play daringly (e.g. if behind). It
should also identify weak areas where we can attack or where we
should defend. Maybe this module doesn't have to be written in C.
Maybe PROLOG, LISP or some other AI language would be better.
* A parameter that makes GNU Go play different styles. Such styles
could be 'play for territory', 'play aggressively', 'play tricky
moves (hamete)', and so on. It could be used to present human
users with different kinds of opponents or to tell GNU Go how to
play certain computer opponents in tournaments.
* Generalize representation and handling of threats so that we have a
graph representation of threats that can be searched to see how
different threats interact.
* An endgame module based on ideas from combinatorial game theory.
To be really useful this would have to deal with early endgame
* Fuseki tuning by hand is difficult. People who are interested
in doing machine learning experiments with GNU Go could try
working with fuseki. This may be one of the areas with most
potential for substantial and reasonably quick improvements.
* Create a paradigm for handling other types of ko (approach move ko,
multi-step ko, etc) and then write code that handles them.
projects / sgk-go / blob