Developments in computer Baduk
Łukasz Lew
Department of Computer Science,
Warsaw University, Poland
e

mail:
lew@mimuw.edu.pl
INTRODUCTION
In most board games humans are dominated by computers. Despite a huge
amount of work Baduk still
remains the last popular board game where computer play is still weak.
The usual Artificial Intelligence approach for game playing consists of a global search algorithm
and a procedure evaluating the goodness of a position. This
does not work in Baduk. A global search is
impossible because the rules of Baduk do not put a lot of constraints on the players thereby allowing an
astronomical number of possible combinations. But even when search is possible, for instance on a 9x9
board
, a good way of evaluating the position is not known. It is so difficult because Baduk positions have
a very rich and diverse structure. Situations appearing on the board can have a lot of subtlety, and may be
in a state of fragile equilibrium, especially
in high level games.
This diversity, complication and deepness are features that distinguish Baduk from other
board
games
. For the same reasons Baduk is considered the greatest challenge for Art ificial Intelligence among
all the games.
This article consi
sts of two parts. First part shortly describes the history of computer Baduk and
the construction of current state

of

the

art Baduk programs. I place special emphasis on a recently
popular Monte

Carlo technique, which is amazing combination of simplicity a
nd good performance (
for
computer Baduk standards
).
The second part presents developments on particular sub

problems of Baduk, such as pattern
systems, Life and Death, finding territory, etc. I place special emphasis on the pattern systems, because
they a
re probably the most interesting and useful for a Baduk player.
PART I
I.1 Short history
The first computer Baduk program was created around 1970 by
Zobrist [
Zor71]. It was mainly based on
computation of an influence radiated by the stones. Thanks
to that it was able to beat a human (an
absolute beginner) for the first time. At that time all the programs were based on different variations of
the influence function.
The next important step was taken thanks to the analysis of human perception of Badu
k [Rei75a,
Rei75b]. Programs of that time started to use an abstract representation of a Baduk board and were able to
reason about strength of groups. But probably the most significant breakthrough happened around 1990
[Boo90] when use of the pattern was w
idely adopted to recognize typical situations and to suggest moves.
Since then probably all top programs combine those three techniques, but little is known about
the details of algorithms used, because most of the best Baduk software is commercial. Those
programs
were usually developed by a single programmer being simultaneously a Baduk player during a period of
5

15 years [Fot92, Fot93]. A notable exception is Gnu Go [Bum05]

a program from the Free Software
Foundation written by a community of programm
ers.
From what is known most of State

of

the

art (non Monte Carlo) programs have the same basic
framework: The backbone is a pattern

based move generator. Also, a lot of local tactical search
determining a life status of strings, connection algorithms joi
ning strings into groups are performed.
Somet imes an influence function is calculated determining definite and potential territory. Somet imes
shallow global search is run, but it is not obligatory. All the gathered pieces of informat ion are used to
choos
e a good move,
It is important to note that all the phases are filled with hand

coded knowledge in the form of
heuristics and specialized algorithms. Usually, even the patterns, the most important component, are
created manually. Such an approach is limit
ed by the amount and the correctness of the knowledge
introduced by the programmer.
I.2 Monte

Carlo Baduk
Recently a new technique, called Monte

Carlo Baduk attracted the attention of many researchers, because
of its simplicity and very good results.
I
n general, Monte

Carlo algorithms are characterized by the fact that randomness is a substantial
ingredient of their calculat ions. In the field of games it usually means that the algorithm plays a large
number of simulations to explore characteristics of a
large search space. This technique usually is used in
imperfect information such as
bridge [
Gin99],
poker [
Bil02] or
scrabble [
She02]. In those games the
randomness is used to model unavailable pieces of information. In stochastic games like
backgammon
[
T
es97] Monte

Carlo is even more natural approach.
In deterministic, complete information games like Baduk this approach seems to be less natural.
It is unclear what elements of a game should be random and why such an algorithm can obtain good
results.
His
torically, the first research on application of Monte

Carlo technique to 9x9 Baduk appeared
in [Bru93]. Bruegmann's approach was remarkably simple. To choose a move, his program run several
hundreds of playouts

completely random games to the bitter end.
At the same t ime, for each legal move,
the program maintained a statistic

an average of the results of those playouts, where this move occurred.
Those averages were used as the estimates of moves' values. Eventually a move with the best value was
played.
Bruegmann approach was followed by ten years later by Bruno Bouzy. Bruno made at least three
contributions described in the following sections. In the fourth section I also describe a different approach
to Monte

Carlo by Tristan Cazenave.
I.2.1 Combinin
g
Monte

Carlo program with a classical approach
Bouzy [Bou03a] set up Indigo2002 (a "classical" Baduk program he developed over a few years) as the
move generator and the Monte

Carlo module as the move evaluator. This way, the Monte

Carlo module
evaluated
less than 10 moves instead of about 300 on a 19x19 board. Obviously moves considered
tactically weak or bad for some reason by the Indigo2002 program were eliminated from evaluation.
This technique is named preprocessing. And its advantage of is improved
speed allowing playing
games on the 19x19 board in a reasonable time and reduction of a number of tactical blunders made by
pure Monte

Carlo.
I.2.2 Monte

Carlo and patterns
The second contribution [Bou03a, Bou05b] is the use of a pseudo

random, biased p
layout, where
probability of playing certain moves depends on their current (mid

playout) surroundings. To implement
it, Bouzy used a database of 3x3 patterns manually built earlier for his program Indigo2002. Second bias
for mid

playout move selection was
detection of dansu which triggered captures with a probability
proportional to the size of captured chain.
I.2.3 Monte

Carlo and tree search
The third contribution [Bou04] is the most influential one, as it is used in all good Monte

Carlo programs
today
. It is a combination of a global selective tree search with Monte

Carlo simulations.
Bouzy's original search algorithm

Progressive Pruning

is an ordinary global game tree searcher with
following extensions. The depth of the tree is first set to one.
When enough children are pruned, the
algorithm extends
the tree to the depth 2 by attaching to all not pruned leaves new children.
Simultaneously, the algorithm performs random playouts starting from current tree leaves (not expanded
nodes of the tree) and
calculated average playouts' results. This average is used to value the leaves and to
prune them, when some of them were found to be statistically inferior to some other leaf in the same
branch. When the program is just about to choose a final move, then
it uses a mini

max principle to
propagate node values from leaves to the root and chooses the final move according to those values.
The combination of game tree search and Monte

Carlo was recently considerably
improved [
Cou06], and
the strongest 9x9 Baduk
programs of the day consist almost of this approach alone.
I.2.4 Monte

Carlo for sub

goal evaluation
Recently Tristan Cazenave proposed[Caz05] a way of integrating Monte

Carlo with specialized, search
algorithms [Caz00, Caz02] used for evaluation of con
nection / disconnection, life / death, capture / escape
or eye making / destroying. For each interesting goal, such as connection of two strings, he maintained a
statistic

average result of those random Monte

Carlo playouts in which the part icular goal w
as achieved.
This way he was able to find the most valuable goals and generate the move, which achieved them. The
strength of play of such algorithm was a much greater than any of its parts alone.
I.2.5 Conclusion
The last example is a completely differe
nt approach than all the previously described, because this time
Monte

Carlo was not used to directly find the best move, but as an auxiliary procedure. It may show to
the reader that Monte

Carlo, despite its simplicity is very flexible and still there is
a lot that can be done.
PART II
Due to the difficulty and diversity of the task of automating Baduk play, many researchers concentrated
on sub

problems of Baduk. This section will browse through the most important, in the author's opinion
,
results. It shows to the reader the diversity and broadness of sub

problems found in the Baduk and the
great variety of existing approaches.
II.1 Pattern
systems
Baduk is generally assumed to be a game where patterns play a major role. Full

board and e
specially
corner openings are an obvious example, but good shape moves and many tactical moves are also typical
pattern moves.
The purpose of a pattern system is to generate good moves for a given Baduk position. For a Baduk
player this is probably the mo
st interesting sub

problem, because such systems are very useful for
assisting in game

analysis. Also pattern systems are usually the most important components of good
Baduk programs.
II.1.1 Pattern
system in Gnu Go
A very good example is an open source
program

Gnu Go. Its pattern system is described in detail in
[Urv02] and in the Gnu Go
manual [
Bum05]. The pattern matching algorithm is based on Deterministic
Finite state Automata (DFA) and therefore it is very fast. The transition graph is created aut
omatically
from a database of manually edited patterns.
The patterns used in Gnu Go are very expressive and can provide detailed informat ion about fuseki
moves, possibilities of: attacking/defending and connecting/cutting a group, and endgame moves. The
p
atterns also provide informat ion about a group's eye

space and the way influence should be propagated
through the board. The biggest bottleneck of the Gnu Go pattern system is that the patterns must be
entered manually.
II.1.2 K

nearest

neighbors
An int
eresting approach is Bouzy's K

nearest neighbor algorithm [Bou05a]. He uses patterns only for
suggesting the next move, but such a limitation makes it possible to harvest and value them automatically.
The resulting system appears to be very flexible. The h
ighlight of th
e paper is presented on diagram
. The
moves played in the opening of the
game,
which
the system played against itself, have a
very high quality and look like the moves
played by a human.
II.1.3 Neural Network pattern system
A different ap
proach was taken by Erik van
der
Werf [
Wer02]. He proposes a neural
network as a tool to recognize patterns and
evaluate moves. He feeds the network with
features of the position such as: location of
nearby stones, proximity to the edge of the
board, liber
ties before and after the evaluated move, possible captures, etc. The network is trained with a
large number of high quality games. The results are very good

the system is able to predict 25% of the
moves in previously unseen professional games.
II.1.4
Explicit
pattern systems
In the previous system the knowledge is encoded in the weights of a neural network. In explicit pattern
systems, the patterns are fixed in size and stored explicit ly together with their ranking. Currently pattern
systems with exp
licit patterns present the best performance in the field.
There are two important pattern
systems:
MoyoGo [Gro06b]

where the system is integrated into a tool supporting Baduk player in
analyzing
games
Bayesian Pattern Ranking [Ste05]

developed at Micr
osoft in cooperation with Cambridge
University
, available on Microsoft online Baduk server.
Pattern system used in MoyoGo was inspired by the research done by David Stoutamire [Sto91]. In turn,
MoyoGo performance inspired David Stern, Ralf Herbrich and T
hore Greapel
from Microsoft.
The main difference between those two systems is the algorithm used for creation of the database
and the scale. Microsoft used a sophisticated Bayesian rating algorithm, while MoyoGo uses a very
simple and robust algorithm. T
he scale difference is that Bayesian system includes over 12 millions
patterns harvested and rated on 181,000 high quality games while MoyoGo includes over 16 millions
patterns harvested and rated on over 500'000 high quality games.
Microsoft's pattern sy
stem is able to predict 34% of moves played in a professional game. This is
quite an achievement compared to 25% of neural network described earlier. MoyoGo's performance is
even better

it predicts 40% of moves. The following part describes MoyoGo patte
rn system.
There are 12 pattern sizes, from very small to the entire board.
Each pattern includes:
1.
The exact stone configuration inside the physical pattern
perimeter, with an empty point in the middle and an
indication which player should play there.
2.
T
he exact distance to the edges of the board.
3.
The Pae

status of the game.
4.
The number of stones in all connected strings of stones
adjacent to the patterns' center point.
5.
The number of liberties of all connected strings of stones
adjacent to the patterns' ce
nter point.
6.
The urgency value (rating).
The pattern system was constructed automatically by processing a huge number of high quality game
records. To decide which patterns should be included, the criteria are:
1.
How urgent (statistically likely) the move i
s, e.g. the sooner a move is played on a patterns
center point, the higher the likelihood that the pattern will be included in the database.
2.
How often a play on the pattern (on its center point) occurs. For quality reasons, exotic patterns
are discarded.
Author of MoyoGo writes:
Earlier experiments with Bayesian learning have proved computationally intensive and too fuzzy
(we require exact statistical data for each pattern, therefore the algorithm for computing the
statistical move likelihood is:
How
often the pattern occurred in all games

How many turns the average player waited to play there
Given a Baduk position system finds all the patterns that match on some place of the board,
i.e.
all
criteria: stones location, pae status, edge distance, etc. are matched. Then several patterns with highest
urgency are selected and given to the user as plausible moves. Because of the relatively large pattern
database, and due to the fact that all releva
nt smaller patterns are included, every Baduk position always
contains several to many recognized patterns.
Strong points of explicit pattern system:
1.
The system matches and classifies patterns near

instantaneously. On a modern PC, a Baduk
position is sca
nned for matching patterns in less than a millisecond.
2.
The system achieves an extraordinary high pro

prediction rate (40% average).
3.
The system is a whole

board context aware and often plays like a pro in positions without much
tactical complexity.
Weak
points:
1.
The system is purely static
, it cannot read ahead so the more complex and hot a tactical situation
is, the worse the system performs.
2.
Pattern shapes do not adapt to the Baduk position, they are fixed.
3.
The system does not take ladders into account.
Author of MoyoGo also describes future work on MoyoGo pattern system:
The system is in constant development, future work includes:
1.
The inclusion of n

th order liberties in the patterns. Order 2 liberties are liberties of
liberties, and are a measure f
or a string of stones to escape enclosure.
2.
Using a larger pro games database.
3.
Using the rank of the player as a heuristic for the reliability of the statistical move
likelihood of the pattern

move.
4.
Strong tactical module.
II.2 Finding
territory
and potential territory
Probably the most well known algorithm for estimating territory and
influence is Bouzy's
mathemat ical morphology [Bou03]. It relies on two
operations borrowed from the image processing domain: erosion and
dilation. The algorithm
is very simple yet it is able to quite precisely
capture the human notion of territory.
The four diagrams above shows
state of the algorithm after each of four dilations, while the diagram on
the side shows the state after additional thirteen erosions
–
the
approximation of territory.
Of course it is purely ``graphical'' and to
produce good estimates it needs information about group strength.
Another algorithm for finding the potential territory is presented in [Ste04]. A board is treated as
a Markov fin
ite field where the propagation of influence occurs. The main difference from the Bouzy's
approach is that the parameters used in the propagation are determined automat ically basing on a large
number of game records.
The same problem was approached in [We
r04] with the use of neural networks. The resulting
algorithm was compared to many methods including the one created by Bouzy. In all the approaches the
results were very similar.
II.3 Scoring
the final position
A simpler problem than finding territory d
uring a game is to score the final position. Sometimes it may be
simple enough to apply exact methods

to prove that the territory is secure. The first such an algorithm
was proposed by
Benson [
Ben76]. It is able to check whether a group is alive uncondit
ionally i.e. whether
it will survive even if the defender will always pass.
An extension of it was proposed by Mueller in [Mue97]. His method created static rules that
together with search provided a proof in the form of a strategy that guaranteed safety
of the territory. He
was able to prove the safety of about a quarter of intersections in a final position.
Mueller's work was followed in [Niu04a, Niu04b]. The methods used for recognizing safe
territories were extended by search

based techniques includin
g region

merging and a method for
efficiently solving weakly dependent regions. This method can prove safe two time more intersections
than the previous algorithm.
But still for practical applications a heuristic method is more useful. A good example migh
t be
an algorithm by Erik van der Werf [Wer03a]. He used a neural network fed with features of classified
groups. His classifier was tested on the same set of 31 positions that was used by Mueller. Only two
positions were classified incorrectly while the n
umber of incorrect ly scored intersections is less than a
half a percent.
II.4 Endgames
Elwyn Berlekamp and David
Wolfe [
Ber97] successfully
applied sophisticated and very elegant Combinatorial Game
Theory (CGT) [Ber82] to a (very) late Baduk endgame. CG
T
evaluates each independent part of the board separately, and
then sum the results to evaluate value of a whole board. This is
possible by taking into account a possibility of t wo or more
consecutive moves of the same player.
As a simple exercise, a read
er may try to
guess which
move in position shown on diagram, (a) or (b), is better
.
Intuition sug
gests that (a) should be better,
b
ecause the threat to rescue the stones is bigger and will come
sooner, but actually (b) is better. If white will play a move
(c) then black may play at (b)
a
mirror white
strategy gaining a draw. But if black will answer at (a) than reader may check that white have a forced
line to win by one point.
The power of CGT allows
for
solving
problems that even a several Japanese and C
hinese 9

dan
players were unable to solve.
II.5 Small
boards
Few years ago 5x5
Baduk [
Wer03c] was
solved by a computer for every opening
move. It is not a surprise that playing in the
centre allows to control the whole board (but
it's not trivial e
ither). In contrast 2x2 and 2x3 opening moves
result in surprisingly even game with many pitfalls.
A similar analysis was earlier performed and published in
a series of art icles by Cho Chikun 9

dan. The computed solution
showed one important sequence that
even Cho missed. The
diagrams
in the first row show
a
n
optimal line of play while the diagrams in the second row show
possible spectacular mistakes.
II.6 Life
& Death
Life and death is one of the sub

problems where a computer works well. It is one of a
few very well
defined sub

problems of Baduk. The problem consists of a description of a Baduk position and the
question: ``Can these stones live?'' which means: ``Is there a way to save a part icular group of stones
regardless of an opponent's play?''. A p
ractical question is: ``How to play to save these stones if it is
possible?''
Computers work well, when the group in question is completely surrounded and the number of
empty intersections (The number of empty intersections usually is an upper bound for t
he number of legal
moves.) is small.
A good example is a program made by
Thomas Wolf

GoTools [
Wol94, Wol99]. It
solves life and death problems with speed
equivalent to an amateur high dan player, so
programs in this area are much stronger than in
gener
al play. Such performance is achieved by a
brute

force search aided with a large number of
heuristics.
GoTools is also able to create Baduk problems. An example is given on diagram.
The weak point of GoTools is that it needs the questioned group to be com
pletely surrounded,
and the number of empty intersections not too big, to avoid a combinatorial explosion of possible
variants.
Recently, Akihiro Kishimoto [Kis03, Kis05] adopted a different search algorithm

proof number
search[All94] to solve a ``one e
ye'' life and death problems and obtained good results.
A different, more theoretical approach was taken by Howard Landman [Lan96]. He applied
Combinatorial Game Theory (CGT
) [
Ber82] to evaluate the number of eyes the group has in each area. It
is natural
that group may have one or t wo
eyes;
even a half eye is a natural term for humans. Applied
CGT may prove that number of eyes in some situations may be equal to 3/4 or 1 1/4. If the sum of such
numbers is equal or greater than two, then CGT proves that the
group is alive.
All of the mentioned techniques are dynamical and exact. I.e. the algorithms are based on search
and their answer is always correct. Another possible approach is a soft one, concentrating rather on the
fast finding of good solutions in th
e majority of cases while allowing few mistakes rather than trying to
prove the solution in each problem. Such approach is usually more practical.
Good examples of a soft and static approach on life and death problems are
works of Chen
[Che99] and Erik va
n der Werf [Wer03b]. Chen replaces the complex search tree by a set of static
heuristics

rules that, when applied to a particular problem, classify each point of the eye

space into one
of several categories allowing relatively accurate estimation of a nu
mber of true eyes.
The work by Erik is an example of a "fuzzy" approach. His algorithm, given a life and death
problem, for each chain in the problem, evaluates values of several easily computable geometric features
such as a number of stones, liberties,
bents, split points, etc and feeds the results to an artificial neural
network to obtain information about the group's state. The network is trained on a large number of
examples and is capable of predicting fate of some chains. The results of such a predi
ction are taken into
account in prediction of
neighbor
chains. Such an approach applied to human game records obtained from
Baduk servers classifies correctly 85%

95% of chains.
II.7 Conclusion
Baduk is a very difficult domain for computers. A various
Baduk sub

problems are researched separately
with an aim to create a strong program in the future. Currently the most active and promising research
directions are Monte

Carlo and large pattern systems. But the domain is very diverse and it is difficult to
foreseen directions of further research and results. It is possible that during the next hundred years we will
not be able to create a very strong computer player, who will be worthy of a human professional. But it is
also possible that there will be a bre
akthrough in nearest months. This unpredictability renders the
computer Baduk a very interesting research domain.
BIBLIOGRAPHY
[All94]
Victor L. Allis. Searching for Solutions in Games and Artificial Intelligence. PhD thesis,
University of Limburg, 199
4.
[Ben76]
David B. Benson. Life in the game of Go. Information Sciences, 10:17

29, 1976.
[Ber82]
Elwyn Berlekamp, John Conway, and Richard Guy. Winning Ways. Academic Press,
1982.
[Ber97]
David Wolfe, Elwyn R. Berlekamp. Mathematical Go : Chilling Gets
the Last Point. A K
Peters Ltd, 1997.
[Bil02]
D. Billings, A. Davidson, J. Schaeffer, and D. Szafron. The challenge of poker. Art ificial
Intelligence, 134(1):201

240, 2002.
[Boo90]
M. Boon. A pattern matcher for Goliath, Computer Go, 13, 1990.
[Bou01]
Bruno Bouzy and Tristan Cazenave. Computer Go: an AI oriented survey, 2001. Preprint
for article in AI Journal.
[Bou03]
Bruno Bouzy. Mathematical morphology applied to computer go. IJPRAI, 17(2), 2003.
[Bou03a]
Bruno Bouzy. Associating domain

dependen
t knowledge and Monte Carlo approaches
within a Go program. Information Sciences, 2003.
[Bou03b]
Bruno Bouzy and Bernard Helmstetter. Developments on Monte Carlo Go, 2003.
Submitted to ACG10.
[Bou04]
Bruno Bouzy. Associating shallow and selective globa
l tree search with Monte Carlo for
9x9 Go. In 4rd Computer and Games Conference, Ramat

Gan, 2004.
[Bou05a]
Bruno Bouzy and Guillaume Chaslot. Bayesian generation and integration of k

nearest

neighbor patterns for 19x19 Go. In G. Kendall and Simon Lucas,
editors, IEEE 2005
Symposium on Computational Intelligence in Games, Colchester, UK, pages 176

181,
2005.
[Bou05b]
Bruno Bouzy. Associating domain

dependent knowledge and Monte Carlo approaches
within a Go program. Information Sciences, 2005.
[Bru93]
Bernd Bruegmann. Monte Carlo Go, 1993.
[Bum05]
Daniel Bump, Farneback Gunnar, and Arend Bayer.
Gnu go, 2005.
[Caz00]
Tristan Cazenave.
Abstract proof search, 2000. Submitted paper.
[Caz02]
Tristan Cazenave. A generalized threats search algorithm. In Co
mputers and Games 2002,
Edmonton, Canada, 2002.
[Caz05]
Tristan Cazenave and Bernard Helmstetter. Combining tactical search and monte

carlo in
the game of go. In IEEE Symposium on Computational Intelligence and Games, 2005.
[Che99]
Zhixing Chen and Ken
Chen. Static analysis of life and death in the game of Go.
Information Sciences, 121:113

134, 1999.
[Cou06]
Rémi Coulom. Efficient selectivity and backup operators in Monte

Carlo tree search.
Submitted to CG 2006, 2006.
[Fot92]
David Fotland. Many Fac
es of Go. 1st Cannes/Sophia

Antipolis Go Research Day,
Februar
1992.
[Fot93]
David Fotland. Knowledge representation in The Many Faces of Go, 1993. Available by
Internet.
[Gin99]
Matthew Ginsberg. Gib: Steps toward an expert

level bridge

playing progra
m. In
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence.
Information Processing Society of Japan, 1999.
[Gro06a]
Frank de Groot, personal communication, 2006.
[Gro06b]
MoyoGo, www.moyogo.com, 2006.
[Kam02]
Piotr Ka
minski. Las Vegas Go.
Available by Internet, 2002.
[Kis03]
Akihiro Kishimoto and Martin Mueller. Df

pn in Go: An application to the one

eye
problem. In Advances in Computer Games 10, pages 125

141. Kluwer Academic
Publishers, 2003.
[Kis05]
Akihiro Kish
imoto and Martin Mueller. Dynamic Decomposition Search: A Divide and
Conquer approach and its application to the one

eye problem in Go. In IEEE Symposium
on Computational Intelligence and Games (CIG'05), pages 164

170, 2005.
[Lan96]
Howard Landman. Eyes
pace Values in Go, pages 227

257. Cambridge University Press,
1996.
[Mue97]
Martin Mueller. Playing it safe: Recognizing secure territories in computer Go by using
static rules and search. In Game Programming Workshop in Japan '97, MATSUBARA, H.
(ed.),
Computer Shogi Association, Tokyo, Japan, 1997.
[Niu04a]
Xiaozhen Niu and Martin Mueller. An improved safety solver for Computer Go.
In
Computers and Games 2004, Ramat

Gan, Israel, 2004.
[Niu04b]
Xiaozhen Niu.
Recognizing safe territories and stones in
computer Go. Master's thesis,
Department of Computing Science, University of Alberta, 2004.
[Rei75a]
Walter Reit man, James Kerwin, Robert Nado, and Bruce Wilcox. Goals and plans in a
program for playing Go. In ACM Annual Conference, 1975.
[Rei75b]
Bru
ce Wilcox Walter Reit man.
Perception and representation of spatial relations in a
program for playing Go. In ACM Annual Conference, pages 37

41, 1975.
[She02]
B. Sheppard. World

championship

caliber scrabble. Artificial Intelligence, 134(1):241

275, 200
2.
[Ste04]
David Stern, Thore Graepel, and David J.C. MacKay. Modelling uncertainty in the game
of Go. In Advances in Neural Information Processing Systems 17, 2004.
[Ste05]
David Stern, Ralf Herbrich, and Thore Graepel. Bayesian pattern ranking for mov
e
prediction in the game of Go. Draft, 2005.
[Sto91]
David Stoutamire. Machine Learning, Game Play, and Go. PhD thesis, Case Western
Reserve University, , Case Western Reserve University, 1991.
[Tes97]
G. J. Tesauro and G. R. Galperin. On

line policy i
mprovement using monte

carlo search.
In Advances in Neural Informat ion Processing Systems: Proceedings of the 1996
Conference. Cambridge, MA. MIT Press., 1997.
[Urv02]
Tanguy Urvoy. Pattern matching in Go with DFA, 2002.
[Wer02]
Erik van der Werf, Jos
Uiterwijk, Eric Postma, and Jaap van den Herik.
Local move
prediction in Go. In 3rd International Conference on Computers and Games, Edmonton,
2002.
[Wer03a]
W.H.M. Uiterwijk E.C.D. van der Werf, H.J. van den Herik.
Learning to score final
positions in
the game of Go. In 10th Advances in Computer Games conference, pages
143

158, 2003.
[Wer03b]
M.H.M. Winands E.C.D. van der Werf, H.J. van den Herik, and J.W.H.M. Uiterwijk.
Learning to predict life and death from Go game records. In Ken Chen et al., ed
itor,
Proceedings of JCIS 2003 7th Joint Conference on Information Sciences, pages 501

504,
Research Triangle Park, North Carolina, USA, 2003.
JCIS/Association for Intelligent
Machinery.
[Wer03c]
Erik van der Werf, Jaap van den Herik, and Jos Uiterwijk
.
Solving Go on small boards.
ICGA Journal, 26(2):92

107, 2003.
[Wer04]
J. W. H. M. Uiterwijk E.C.D. van der Werf, H.J. van den Herik. Learning to estimate
potential territory in the game of Go. In 4th International Conference on Computers and
Games (C
G'04), 2004.
[Wol94]
Thomas Wolf. About computer go and the tsume go program Gotools, 1997. Based on an
article from 1st game programming workshop, hakone, Japan, 1994.
[Wol99]
Thomas Wolf. Forward pruning and other heuristic search technics in tsume go
, 1999.
[Zor71]
Albert L. Zobrist. Complex preprocessing for pattern recognition.
In ACM annual
conference 1971, 1971
.
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