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Computer Recognition of Aesthetics in a Zero-sum Perfect Information Game


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Zero-sum perfect information games are those where all the moves are known to every player. Examples include chess, go and noughts and crosses. This research intended to see if aesthetics within such a domain could be formalized for machine recognition since it is often appreciated and sought after by human players. For this purpose, Western or International chess was the most suitable because there is a strong body of literature on the subject, including its aesthetic aspect. Eight principles of aesthetics were identified and formalizations derived for each to form a cumulative model of aesthetics. A computer program that incorporated the model was developed for testing purposes. Two novel experiments were then performed comparing thousands of chess compositions (where aesthetics is generally more prominent) against regular games (where it is not) and the results suggest that computers can recognize beauty in the game. Possible applications of this research include more versatile chess database search engines, better automatic chess problem composers and computational aid to judges of composition and brilliancy tournaments. In addition, the methodology applied here can be used to gauge aesthetics in similarly complex games such as go and generally to develop better game heuristics.
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Computer Recognition of Aesthetics in a Zero-sum Perfect Information
1College of Information Technology, 2Vice-Chancellor’s Office
Tenaga Nasional University
Km 7, Jalan Kajang – Puchong, 43009 Kajang, Selangor
Abstract: - Zero-sum perfect information games are those where all the moves are known to every player.
Examples include chess, go and noughts and crosses. This research intended to see if aesthetics within such a
domain could be formalized for machine recognition since it is often appreciated and sought after by human
players. For this purpose, Western or International chess was the most suitable because there is a strong body
of literature on the subject, including its aesthetic aspect. Eight principles of aesthetics were identified and
formalizations derived for each to form a cumulative model of aesthetics. A computer program that
incorporated the model was developed for testing purposes. Two novel experiments were then performed
comparing thousands of chess compositions (where aesthetics is generally more prominent) against regular
games (where it is not) and the results suggest that computers can recognize beauty in the game. Possible
applications of this research include more versatile chess database search engines, better automatic chess
problem composers and computational aid to judges of composition and brilliancy tournaments. In addition,
the methodology applied here can be used to gauge aesthetics in similarly complex games such as go and
generally to develop better game heuristics.
Key-Words: - aesthetics, chess, game, intelligence, beauty, composition, zero-sum, heuristic, evaluation
1 Introduction
A zero-sum perfect information game is a useful
research domain because in theory, the entire game
tree can be computationally generated with nothing
left to chance. This is easy in games like noughts
and crosses but more difficult for chess or go
because the number of possible positions (or nodes)
is much higher. The interesting thing about some of
these games is that there is more to them than just
winning (often best accomplished through brute-
force analysis). This means that concepts and ideas
that are synergetic or beyond the simple rules of the
game can be analyzed using a discrete or formalized
approach. In chess for example, aesthetics is also
important and appreciated not only by grandmasters
but average players as well.
Garry Kasparov - arguably the world’s strongest
player - is reported to have said, “I want to win, I
want to beat everyone, but I want to do it in style!
[1]. Computers currently play chess at the
grandmaster level and have even defeated the world
champion but they cannot tell a beautiful move
sequence or combination from a bland one. This is
because the objective has always been simply to win
[2-4]. Computers are hence unable to compose chess
problems like humans do or simulate appreciation of
the game. Chess literature adequately covers its
aesthetic aspect (refer section 2) and the research
presented here was intended to see if this
information could be formalized for computational
purposes, not unlike heuristics used in game
The result is a model of aesthetics based on the
proposed formalizations of beauty principles in
chess. It is potentially capable of giving computers
the ability to recognize aesthetics in the game
comparable to the way humans do. Section 2
reviews some of the relevant contributions to the
area. Section 3 details the proposed formalizations
and aesthetics model. Section 4 presents some
experimental results intended to validate the model.
A discussion on the results and related issues
appears in section 5. The paper concludes with a
general summary of the ideas presented.
There are over 700 million chess players and
composers worldwide [5]. Therefore the authors
believe this research presents significant findings
with respect to AI within at least the domain of
chess itself. Extensions to other games or areas of
research are not fully explored in the limited space
available here but a brief discussion on it is
presented in subsection 5.1. The information that
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follows is therefore specific to chess given the
inextricable nature of aesthetics to its domain.
2 Review
One of the earliest formal references to the
aesthetics of chess was by former world champion
Emanuel Lasker. In his book, “Lasker’s Manual of
Chess” he devoted an entire chapter to it [6]. There
he writes of the concept of “achievement” (e.g.
winning material, space, the game itself) being
important to aesthetics and that comprehension of
the game, not necessarily mastery, is all that is
required for its appreciation. Margulies, a
psychologist, derived experimentally eight
principles of beauty in the game from the judgement
of experienced players, as follows [7].
1. successfully violate heuristics
2. use the weakest piece possible
3. use all of the piece’s power
4. give more aesthetic weight to the critical
5. use a giant piece in place of minor ones
6. employ chess themes
7. avoid bland stereotypy
8. neither strangeness nor difficulty produces
Similar criteria have been mentioned in other
sources with notable additions [8-15]. For example,
paradoxical maneuvers (e.g. sacrificing pieces) and
being economical in their use are considered
aesthetically pleasing as well. Levitt and Friedgood
include the concepts of geometry and flow as
additional elements of beauty in the game [11].
Geometry implies graphic effects such as alphabets
formed on the board whereas flow refers to forced
play rather than many confusing alternate variations.
Aesthetics is not limited to compositions and is also
found in real games (e.g. tournaments), though less
prominently [12-14]. Brilliancy prizes are even
awarded at certain tournaments to games that are
aesthetically noteworthy either in full or part [15].
Even though most composition conventions (e.g.
include variations, no duals, no symmetry) do not
apply to real games, aesthetics is inherently shared
between the two domains as long as the rules are the
same [16]. Given say, direct-mate compositions
(mate in n moves against any defense), they
essentially only differ with real games in terms of
perceived beauty. Experienced players can often
easily tell if a position looks like a composition
because it is too “unusual” or “convenient” to have
occurred in a real game. They are also generally in
agreement with composers about what constitutes
beauty in the game [17]. Computationally, aesthetics
has been largely left to humans. For example,
computers are capable of deriving forced checkmate
combinations by constructing a complete database
(e.g. from a set of desired pieces) and working
backwards one ply (half-move) at a time but not
capable of any “creative” activity per se [18][19]. It
is left to humans to judge if the combinations are
beautiful despite being conventionally acceptable or
“correct” from a composition standpoint.
Composition conventions are often used to
benchmark chess problems with little explicit
emphasis on aesthetic factors [20][21]. The two (i.e.
conventions and aesthetics) are distinct but not
mutually exclusive [22]. Real games for example,
also exhibit aesthetic properties but do not adhere to
most composition conventions (usually in excess of
20 “rules”) [23]. Previously, mainly chess themes
(e.g. Grimshaw, Pickaninny, direct battery), as a
principle of beauty, had been weighted for the
purpose of automatic problem composition and this
was done by consulting one or two master
composers [20][21]. The values ascribed to those
themes - typically exotic ones used in chess
compositions and seldom in real games - were
arbitrary and based primarily on experience.
This meant that some themes were preferred over
others and that some or all themes might have to be
weighted again if new ones were added since their
values were relative to one another. Additionally, all
implementations of a particular theme were valued
equally even though some configurations would no
doubt be more beautiful than others [24]. Walls
showed that beauty principles performed better than
regular chess heuristics in solving certain types of
chess problems [25]. He adapted a selection of
Margulies’ principles as beauty heuristics but used
them to guide a game playing engine instead of
evaluating the principles themselves. They were
therefore merely identified as either being present or
absent in a particular line of play.
The formalizations used for those principles
however, were rather rudimentary to minimize
computational load. So in terms of say, distance (or
using all of a piece’s power), a queen moving a
certain number of squares across the board was
considered just as beautiful as a rook or bishop. For
this research, weighting individual principles
through supervised or unsupervised learning was not
suitable because reliable test data (i.e. aesthetically
rated positions) is scarce. Varying implementations
of a particular principle are then also difficult to
properly account for [26]. In addition, it was
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considered unnecessary since chess is a limited and
precise domain with its own established measures
and units that are not typically subject to personal
taste in the way that say, images are. In the latter
case, linear regression or classification can be used
to individually weight aesthetic features since there
are no agreed standards for rating them [27].
The approach taken in this research is more akin
to how the aesthetics of music is sometimes
calculated, where discrete representations (e.g.
frequency of notes, intervals) of particular attributes
(e.g. pitch, volume) are used to recognize beautiful
compositions [28][29]. However, chess is a more
limited and less culturally-dependent domain than
music so formalizations based on established
metrics are probably more reliable. The next section
describes in detail the metrics, chosen principles and
scope of analysis.
3 Methodology
In 1950, Claude Shannon explained how a computer
could be programmed to play chess using estimated
values (i.e. pawn units) of the chess pieces (K=200,
Q=9, R=5, B/N=3, P=1) so that a score for every
position in the game tree could be obtained based on
the amount of material captured [4]. The king is
actually of “infinite” value since its capture means
losing the game but for practical programming
purposes, it is often valued significantly higher than
all the other pieces combined. Alan Turing is often
credited along with Shannon for proposing a
computational approach to playing chess. Two of his
piece values however, were slightly different (i.e.
Q=10, B=3.5) and are not as frequently used [30].
Computers could therefore be programmed to
decide which moves were the most favourable from
a material standpoint and play a reasonable game of
chess based on the piece value property and pawn
unit metric. Modern chess programs essentially still
employ Shannon’s (Type B) method and rely on
material as a primary factor for evaluation. To
improve performance, piece values are sometimes
changed by a program during the course of a game
based on positional considerations [3]. For this
research, the standard Shannon piece values were
used; the king however, was valued at 10 pawn units
to be aesthetically in line with the other pieces.
“Mating squares” or squares onto which
occupation by an enemy piece would result in
checkmate are also legitimate threats and valued
equivalent to the king. In aesthetic analysis, winning
is considered a prerequisite - no beauty is found in
losing - and there is no intention of driving actual
game play. Aesthetic evaluation of a chess
combination is done ex post facto or in retrospect on
the completed move sequence to determine how
beautiful it was. The squares of the chessboard itself
are used as a metric to evaluate properties like
distance and piece power (mobility) because more
powerful pieces tend to control more squares [31].
Distance is measured as the number of squares
between two pieces on any line (i.e. ranks, files or
diagonals). If there are three squares between two
pieces, the distance is calculated as four. Piece
power is interpreted here as the maximum number
of squares a piece could possibly control on an
empty board and was found to be: king (8), queen
(27), rook (14), bishop (13), knight (8) and pawn
(4). The pawn’s power is based on the fact that it
can capture one square to the left or right and move
forward one square or two for a total of four. Piece
power is used to derive slightly different values for
identical maneuvers performed by different pieces.
It is based on their inherent and relative importance
as generally perceived in the game.
3.1 Selected Principles and Scope
Based on the literature surveyed (refer section 2),
eight aesthetic principles in chess were identified
and selected namely: successfully violate heuristics,
use the weakest piece possible, use all of the piece’s
power, win with less material, sacrifice material,
checkmate economically, spread out the pieces
(sparsity) and use chess themes. Margulies’ 4th
principle was not explicitly included because it
simply means to emphasize the role of the “active”
(e.g. checkmating) piece in a move sequence. His
5th principle used imaginary pieces not within the
scope of Western chess while the 7th and 8th
principles tend to rely on previous knowledge and
experience so they could not be included.
Geometry was not included because it is very
rare, even in compositions whereas flow is
somewhat biased against compositions that typically
feature many side variations (and are even lauded
for it by convention). The goal of this research was
to evaluate aesthetics in both domains (real games
and compositions) but only in aspects that are
equally applicable. Given the vast possibilities in
chess and feasibility issues, aesthetic evaluation was
limited to orthodox direct mate-in-3 move
sequences. This scope of analysis permitted access
to a wide selection of chess compositions and
combinations from tournament games. The
evaluation function of each principle was designed
based on the metrics and properties (refer section 3)
to score a theoretical maximum value of
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approximately 1 so there would be no arbitrary
preference given to any principle. There was nothing
in the literature surveyed to suggest that some
principles are inherently better than others. Only in
extreme cases of certain principles is this limit
markedly exceeded. Normalizing to 1 would involve
multiplying the scores with an arbitrary value and
was thus avoided. For brevity and by convention,
white is always assumed to be the winning side.
Checkmates - though preferably forced (like in
direct-mate compositions) - are also considered
beautiful even if they are not forced. A beautiful
mating combination in a real game for example, is
often due to the oversight of the opponent. For that
reason it might be perceived by humans as less
beautiful but only upon deeper analysis and this
would have little to do with the beauty of the moves
themselves [15]. A composition of the direct-mate
variety however, would in such a case be invalidated
by convention for being “incorrect”, not unaesthetic.
Selfmate problems for example, require that both
sides cooperate to checkmate black, primarily
because certain (aesthetic) effects are not possible
with direct-mates [32]. The selected principles and
rationale behind their proposed formalizations are
explained in the following subsections.
3.1.1 Violate Heuristics Successfully
Heuristics in chess are typically general rules that
govern good play. A move that violates one or more
heuristics is considered paradoxical if it results in an
achievement of some kind (e.g. checkmate). Given
the scope, four heuristics were selected for
evaluation: keep your king safe, capture enemy
material, do not leave your own pieces en prise (i.e.
in a position to be captured) and increase mobility of
your pieces. Other heuristics such as control the
center and avoid doubled pawns were not included
because the effects of violation are not as obvious in
the short-term [33][34]. A violation of keep your
king safe was defined as moving the king to a square
which makes it prone to check on the next move.
If the king’s destination is within the center four
squares of the chessboard, it counts as a complete
violation and scores 1 full point. The next
surrounding 12, 20 and 28 squares are worth 0.75,
0.5 and 0.25 points respectively. This is because
there is greater risk of exposure as the king
approaches the center. Not capturing enemy pieces
that are exposed and could be captured
advantageously also counts as a violation. Given the
depth and complexity of some exchange sequences
in chess and the related positional dynamics, only
undefended pieces or defended ones worth more
than the capturing piece qualify. A non-capturing
move or one that prefers a piece other than the most
valuable available violates this heuristic. Pawns do
not count as pieces worth capturing because they are
usually not worth the diversion and fall short of
what is required for a decisive advantage in chess
(i.e. 1.5 pawns between grandmasters) [35]. The
score for this violation is calculated as the sum of
the value of uncaptured enemy pieces divided by the
value of the queen. Therefore a full point is scored
in cases where a queen or pieces of equivalent value
are not captured in favour of some other move.
Like the previous violation, leaving your own
pieces en prise applies only to pieces and not pawns.
There is no violation if the move played captures an
enemy piece worth more than the one left en prise or
if the friendly piece is favourably defended (no
potential loss of material). Exchanges were analyzed
to a depth of two plies. The score is calculated as the
sum of the value of en prise pieces divided by the
value of the queen. The last violation is decreasing
your own piece mobility. Usually, players try to
control more squares with their pieces but
sometimes the opposite is done and this can be
paradoxical. For example, a queen or bishop may be
moved to the very corner of the board behind some
friendly pieces where its mobility is greatly reduced
or moved to block several other pieces, reducing
overall mobility.
The score is calculated as: (w1-w2)/w1. Here, w1
and w2 denote the number of legal moves for white
in the initial and subsequent position respectively
(assuming for a moment, black skipped his turn).
Violation occurs if the score is positive. This
principle is determined only after white’s first or key
move because in compositions the first move usually
points to the solution and by convention, the most
surprising to solvers. The overall score for this
aesthetic principle (P1) is formalized as shown in
equation 1. More heuristic violations (n) can be
included as long as their maximum scores are in line
with the others (i.e. 1.0).
v(hn) = value of a particular heuristic violation
It is possible that the presence of more violations in
a move would sometimes actually lower its overall
aesthetic score for this principle since the average is
used. This was necessary to keep the score
consistent with the other aesthetic principles. In
addition, it is arguably the nature of individual
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violations that influences aesthetic perception of this
principle more than the number of violations.
3.1.2 Use the Weakest Piece Possible
This principle means using the weakest piece
possible to achieve a particular objective. Given the
scope, it was refined to mean using the weakest
piece possible to checkmate and therefore applies to
the last move in the combination. The score is
inversely proportional to the piece power of the
checkmating piece. The formalization is given as:
r(p) = piece power
The numerator is set to 4 so that if a pawn is used to
checkmate, the score reaches its maximum of 1. In
the case of a double checkmate (i.e. two pieces
mating the king simultaneously), only the piece that
moved counts. In the rare case of a two-way
discovered checkmate, the weaker of the two pieces
is chosen (refer Appendix).
3.1.3 Use all of the Piece’s Power
Using all of the piece’s power relates to its
efficiency and can be interpreted as the number of
squares a piece traverses in a single move. Traveling
a greater distance is considered more beautiful than
a shorter one. If a weaker piece (e.g. bishop) travels
a certain distance, less of its power is wasted than if
it were a more powerful one (e.g. queen). The
bishop move is therefore considered more beautiful
than the queen move. This principle applies to all
the moves except those of the opponent because
they usually work against the desired achievement
(and hence aesthetics) of the winning side. The score
is calculated as follows.
d(pn) = distance traveled by a piece, r(pn) = that
piece’s power, n = number of evaluation stages (i.e.
each move by white + checkmate)
The knight, given its unique movement, defaults to a
fixed 3 squares. In a mating combination, the
distance between the checkmating piece and the
enemy king in the final position is also evaluated in
terms of this principle. It is possible in certain
positions for the total score to exceed 1 (e.g. two
maximal pawn moves + one knight move + mate
using knight = 1.75) or fall significantly below it
(e.g. two single square queen moves + one single
square rook move + mate using rook right next to
the king = 0.22). Like the previous principle, it
applies to all combinations regardless of how
beautiful or bland they might be.
3.1.4 Win with Less Material
This principle is considered aesthetic mainly
because it is paradoxical. Usually, the side with
more material is likelier to win. It applies only if
black’s total material value exceeds white’s. The
value is calculated as:
w1/b1 = initial material of white/black, m = 38
The denominator is set to 38 because this is the
maximum amount of expendable material for an
army (at least one pawn must be left) where
checkmate is still possible, however unlikely. In
such a case, black would have some material on the
board that would blockade his own king and
facilitate the checkmate. The possibility of such a
combination is interesting in itself. The score is
calculated in the initial position.
3.1.5 Sacrifice Material
Sacrificing material is also paradoxical. It is not
quite the same as violating the heuristic of leaving
your own pieces en prise. Rather, it applies more to
exchanging your pieces in a seemingly unfavourable
way in order to secure a decisive advantage or force
a win. The “romantic” players of the late 18th and
early 19th centuries often used bold sacrifices that
were not always sound to impress spectators [36].
Former world chess champion Mikhail Tal, who
considered chess first and foremost an art, was also
known for intuitive sacrifices that gave rise to
complications on the board and confused his
opponents [37].
Since the late 20th century however, sacrifices are
not as common in tournaments because players –
having trained with computers - can usually spot
weaknesses in them. Computer analysis can also
quickly reveal similar flaws in compositions. Even
so, sacrifices are still employed - even required in
some positions - but are more calculated and
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scrutinized than ever before. The “dramatic effect”
of a sacrifice usually correlates with the amount of
material lost so the function below (equation 5) is
used to calculate the value for this principle. The
material constant depends on the number of moves
there are in the combination.
12 12
5, 9,14,19...
ww bb
w1/w2 = initial/final material of white, b1/b2 =
initial/final material of black, m = material constant
For example, a mate-in-2 sequence would have a
material constant of just 9 because this (a queen’s
value) is the most amount of material that could be
lost to the opponent in one move. The checkmating
move does not count because the opponent cannot
respond. A mate-in-3 would have a constant of 14
since after the opponent’s second move, at most
another rook (given the original piece set) could be
lost and so forth. No sacrifices are possible for mate-
in-1 positions and only positive values apply.
This function takes into account sacrifices of any
number of pieces of any type, including adjustments
for pawn promotions by both sides because the nett
difference in material at the end of the move
sequence will reflect how much material was really
lost. It would be misleading for example, to sacrifice
a knight after the first move only to promote a pawn
to a queen on the second. Negative values indicate
that white actually gained material but this is not
held against him because many mating combinations
necessarily result in significant material loss by the
opponent. They are however, less beautiful.
3.1.6 Checkmate Economically
Economy in chess ideally refers to using the
minimum amount of resources – in most cases this
means material - to achieve a particular objective.
For the scope, the objective is to checkmate the
opponent. This principle is therefore evaluated in the
final position where economy is most often
exemplified [38]. It is difficult to ascertain economy
in the moves preceding the final position because
they may contain sacrifices or in contrast, “quiet”
maneuvers that are necessary but do not make much
use of a piece’s power.
Economy can be formalized as shown in
equation 6. The parameters are essentially based on
the conventions used by the Bohemian “school” (i.e.
style) of composition which is known for its
emphasis on economy [39]. The other two schools
are the Logical and Strategic [11][23]. These do not
neglect economy but often make concessions in the
interest of themes. Due to space limitations, a
detailed explanation of this function and all its
parameters is referred to in [40].
an = control field of a particular active piece,
fn = maximum control field of that active piece,
o = number of overlapping control field square,
fk = standard king’s domain (i.e. 9),
sn = maximum control field of a particular passive
piece, p = number of friendly pieces on the board
3.1.7 Spread out the Pieces (Sparsity)
Positions that are cluttered or crowded are generally
considered less beautiful than those more spaced out
[9]. Two important features when evaluating
sparsity are therefore the number of pieces on the
board and their proximities to each other. Even so, a
position that requires more pieces should not
necessarily suffer in terms of being sparse than say,
an endgame position where pieces are inherently
few. There are several ways that sparsity or its
inverse, density can be evaluated (e.g. like pixels in
a matrix, using quadrant density ratios, counting
symmetries) but they do not translate as well to the
chessboard [41].
For instance, a relatively “dense” quadrant of the
chessboard may be considered sparse if there are
only 3 or 4 pieces there because it is not practical or
useful for them to be spaced out into different areas.
There are also complications when we consider the
centre 4x4 squares of the board as constituting a
“fifth” quadrant because sometimes pieces are
concentrated there. In fact, activity or checkmates at
the center of the board are considered more beautiful
than at the edge or corner [42]. Ideally, evaluation of
this principle should be able to differentiate between
positions to the point of sufficient sparsity, which is
when a position is no longer cluttered or crowded
enough to warrant improvement.
This is the point when the board configuration no
longer plays a role in the evaluation. An example is
say, just three pieces on the 64-square chessboard.
One could imagine many different configurations of
those pieces that would be considered sufficiently
sparse. A more effective method to evaluate sparsity
that works well with chess (and other similar board
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games) was developed and shown in equation 7.
Surrounding pieces are those in the field of a
particular piece (i.e. the squares immediately around
it). Fewer pieces around a particular piece make the
area appear sparser.
s(pn) = pieces surrounding a particular piece
The field is used to ensure that if all the pieces have
at least one square in every direction vacant, the
position is considered sufficiently sparse. This field
can be expanded to two squares or more for larger
boards (e.g. 19x19 in go). The average number of
surrounding pieces is used to provide a better
general idea of how uncluttered a position is. One is
added to the denominator to prevent a division by
zero error where there are no surrounding pieces.
Both black and white pieces are taken into account.
Given that mating combinations often require the
attacking pieces to be in proximity to the enemy
king, this principle is evaluated only in the initial
position before any moves are made.
Fig. 1 Sparsity evaluation of chess positions
Figures 1a and 1b show the sparsity scores of
positions taken from a composition and tournament
game respectively. The higher the score, the sparser
it is. Arbitrarily adding or removing pieces will not
necessarily bring the score down or up. It depends
on how they affect the board configuration.
Evaluations of many different positions suggested
that this function captured the perception of sparsity
in chess better than alternative methods. It can also
be used as a heuristic in game engines to
“intuitively” judge positions. Selected lines of play
can be analyzed at greater depths than possible with
the full set of heuristics and subsequently aid move
selection because sparse positions are generally
associated with less complicated positions whereas
dense ones suggest impending difficulties.
3.1.8 Use Chess Themes
Themes in chess are essentially good tactics.
Common themes include the fork, pin and skewer
whereas more exotic ones - used primarily in chess
problems - include the Grimshaw, Pickaninny and
Plachutta. The effective use of themes is
fundamental to aesthetics in chess. Ten themes
common to both compositions and real games were
selected [11][43][44]. For the time being, only their
presence was taken into account.
T = occurrence of chess theme
The score is calculated as simply the number of
theme occurrences in the move sequence. The
selected themes included the fork, pin, skewer, x-
ray, discovered/double attack, zugzwang, smothered
mate, crosscheck, promotion and switchback. A
single move can contain more than one theme.
Formalizations for each of these themes are under
3.2 Model of Aesthetics
The individual formalizations for the principles
described above are insufficient for evaluating
aesthetics in chess even given the scope, though they
might be capable of identifying highlights of a
particular move sequence. A model of aesthetics is
therefore proposed in the form given below.
A = aesthetic value of a combination, P = aesthetic
principle evaluation score
The sum of scores for aesthetic principles present in
a combination should in theory, be higher for
beautiful ones. It stands to reason that attractive or
“brilliant” move sequences in real games and
compositions should contain not only a higher
frequency of aesthetic principles but better instances
or configurations of them which the formalizations
a) K. Fabel, Deutsche
Schachzeitung, 1st Prize,
1965; sparsity: 0.264
b) Mannion vs Rojas,
Yerevan ol (Men), ½-½,
1996; sparsity: 0.478
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ISBN: 978-960-6766-41-1
proposed are flexible enough to evaluate. The
presence of more principles however, does not
guarantee a high score (their individual evaluations
may be low) and neither does fewer principles
guarantee a low score (individual evaluations may
be high). This model also leaves room for the
inclusion of more aesthetic principle formalizations
than the current eight.
4 Experimental Results
A computer program called CHESTHETICA was
developed incorporating the aesthetics model for
experimental purposes. The program does not
possess any game playing intelligence but is capable
of facilitating a complete match between two
players. This was necessary to set the foundation for
proper evaluation of all the aesthetic principles and
detection of relevant themes. Two experiments were
designed to see if the computer program would rate,
on average, compositions higher than tournament
games in terms of aesthetics; consistent with human
perception of beauty in chess.
For this purpose, a random sample of 10,000
mate-in-3 chess compositions (mostly published and
by professional composers) was compared against a
random sample of 10,000 mate-in-3 combinations
taken from tournament games (refer Appendix).
Since aesthetics in chess tends to correlate with
sound play, only games between master players
(ELO rating 2300) were used. The ELO rating
system is a widely employed method for estimating
the relative skill of chess players where a minimum
rating of 2300 usually qualifies for an FIDE Master
title. Novice or intermediate play would inherently
be less beautiful and bias the results.
While most master games tend to end with one
player resigning (as opposed to being checkmated)
the wide availability of games provided a sufficient
resource for the kind required. Most resigned master
games however, are not so close to checkmate. The
tournament game combinations used in the
experiments were not necessarily forced mates like
in the compositions because this does not influence
its aesthetic evaluation in any way. The important
thing is that the mates were played out in full by
humans and not generated artificially by a computer.
Figure 2 shows the results obtained. They have
been sorted in descending order for clarity. The
compositions scored an aesthetic mean of 3.04 (SD
1.05) whereas the tournament games scored a mean
of 1.92 (SD 0.94). A two-sample t-test assuming
unequal variances showed the difference in means to
be statistically significant; t(19741) = 79.7, P<0.001.
Since themes did not have individual formalizations,
a second experiment was performed excluding them.
1 2001 4001 6001 8001
Tournament Games
Fig. 2 Aesthetic scores of compositions vs.
tournament games
This time the compositions scored a mean of 2.34
(SD 0.72) compared to 1.41 (SD 0.55) for the
tournament games. A two sample t-test assuming
unequal variances showed that the results were also
statistically significant; t(18776) = 103.5, P<0.001.
Including themes, the mean was 58% higher for
compositions and without them, 66% higher. The
implications of these results and possible
applications of this research are discussed next.
5 Discussion
The statistically significant differences in means
found between the overall aesthetic scores of chess
compositions and tournament games suggest that
aesthetics in chess can be recognized
computationally. The wider disparity seen between
the means of both groups without taking into
account the presence of themes suggests that
individual formalizations help capture aesthetics
better. This does not mean that compositions
necessarily score higher than real games in terms of
beauty because there are always exceptions such as
poorly composed problems and overrated
combinations in real games.
Figure 2 clearly shows that there are
combinations in real games that in fact score higher
than some compositions. Nevertheless, a high score
based on the aesthetics model proposed would likely
point to a move sequence that humans would find
beautiful. Experiments involving human players
were not performed because their knowledge of
what constitutes beauty in chess would be difficult
to ascertain as reliable (e.g. like that found in chess
literature). However, a positive correlation between
scores based on the model and those of competent
(not necessarily master) human players would
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ISSN: 1790-5109
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ISBN: 978-960-6766-41-1
validate the model even further. This remains to be
done. It is difficult to determine if shorter or longer
move sequences would exhibit similar aesthetic
scores because shorter ones tend to be quite simple
(and limited thematically) whereas longer ones can
be difficult for humans to follow. Comparisons
between combinations of different lengths are not as
reliable for the same reason [33].
Modifications or extensions to the model and
individual formalizations could be applied where
necessary to compensate for these possible
discrepancies. Chess database search engines can
incorporate the aesthetics model proposed to locate
aesthetically pleasing combinations in vast databases
of games for human appreciation and study. They
can also employ the individual evaluation functions
to gauge certain features in games. Automatic
problem composers can use the formalizations
presented to refine their fixed-value approach to
aesthetics and to decide - without human
intervention - which derived forced checkmates are
the best.
In addition, chess composition and brilliancy
prize judges might find some impartial assistance
through this model when deciding on a winner [45].
Finally, complex compositions could more quickly
be solved - and in certain cases even solved at all -
if game engines also employed heuristics based on
aesthetics. Traditional heuristics used to prune the
game tree in conjunction with brute-force searching
sometimes cause paradoxical but necessary key
moves to be missed [25].
5.1 Aesthetics in Other Games
Investigations into the game of chess have
sometimes unintentionally yielded benefits in other
domains and research areas. The game itself should
therefore not be seen as little more than a stepping
stone toward greater things [16]. With millions of
players worldwide and constant efforts to improve
computer playing ability, the work presented here
hopefully paves the way toward another fertile area
of research and inquiry. It demonstrates that there is
still much to learn about what humans like and
experience in the game.
This can be facilitated through the use of
computers for aesthetic analysis in the same way
that rigorous computer analysis of the game tree has
revolutionized many of our old ideas. Unlike
checkers, chess is still far away from being solved
[46]. AI researchers have nothing to be ashamed
about if their research into chess translates into
technologies that mainly have the potential of
enriching the experience of human players.
Nevertheless, other zero-sum perfect information
games such as go could apply the same
methodology used here to develop their own
aesthetics models once there is enough literature to
substantiate them [47]. A direct application of the
formalizations presented here is not immediately
possible (with the exception of sparsity) because
aesthetics in such games is inextricably linked to the
rules which govern them. For example, in (Western)
chess there are 6 piece types whereas in go there is
only one. Therefore visual pattern recognition would
most likely be more significant to the aesthetics of
go than it is in chess.
Economy on the other hand would be much less
about piece values than mobility given the objective
of go which is to control more territory on the board.
Like in chess, the aesthetics of go is associated with
sound play and could contribute to the development
of better game playing technologies [48]. In the near
future, computers might be able to defeat even the
strongest human go players [49]. Then, attention
could shift to aesthetics for its own sake like is being
done now in chess. Chess variants, estimated at over
1000 in number, would be more amenable
aesthetically to the formalizations in this paper
because only minor modifications would be required
to adapt them [50].
Many variants were in fact, created due to
aesthetic limitations in the orthodox game. For
instance, variants that use fairy chess pieces
(unorthodox ones not in the standard set) or different
board types could easily derive their piece values
and power following the methods described in
section 3. Finally, it is difficult to say if aesthetic
recognition in board games could also contribute to
the humanization of otherwise bland and “brutal”
game playing software. Associating a kind of
emotional response in programs that would favour
say, making a beautiful move - even when it is not
necessarily the most effective one - could bring us a
step closer toward that objective. The experience of
playing against a computer might then be
comparable to that of a human opponent.
6 Conclusion
In this paper, formalizations for established aesthetic
principles in chess were proposed and presented
cumulatively as an aesthetics model for the game.
These formalizations used mainly the inherent
metrics and properties of the game instead of
arbitrarily assigned weights [35]. The aesthetics
model was incorporated into a computer program to
compare large random samples of orthodox direct
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ISBN: 978-960-6766-41-1
mate-in-3 compositions and similar combinations
from master-level tournament games. The results
showed a statistically significant difference in their
means suggesting that computers can use the model
to recognize beauty in the game. The aesthetics
model can be further enhanced by including
formalizations of additional aesthetic principles and
individual formalizations for a variety of chess
themes. Work on the latter in currently in progress.
Chess literature places no emphasis on particular
aesthetic principles so not weighting them
individually minimizes bias. Applications of this
research are most obvious within the domain of
chess but extensions to other games of similar
complexity are feasible. Researchers in other areas
might also draw on the work presented here in
creative ways since chess is a useful and popular
domain of investigation. With sufficient processing
power, it is quite possible that computers will one
day be able to discover amazing and brilliant
combinations for human aesthetic appreciation and
study that would otherwise take centuries to occur in
real games or be thought of by composers.
7 Acknowledgements
I would like to thank John McCarthy (Stanford),
Michael Negnevitsky (University of Tasmania),
Jaap van den Herik (Universiteit Maastricht), John
Troyer (University of Connecticut), Malcolm
McDowell (British Chess Problem Society), Brian
Stephenson (Meson Database), GM Jonathan Levitt
and IM David Friedgood for their comments and
feedback. I would also like to thank the University
of Malaya for their support and cooperation.
8 Appendix
Two-way discovered checkmate example.
FEN: 1r6/2p1R3/3k4/1P1P4/7r/8/6n1/1KBR4 w
(1. Ba3+ Rb4+ 2. Bxb4+ c5 3. dxc6++)
Chess problems obtained from Meson Database
(74513 problems);
FIDE Master tournament games obtained from
ChessBase MegaDatabase 2007 (3512846 games);
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ResearchGate has not been able to resolve any citations for this publication.
Full-text available
We discuss the application of the Zipf-Mandelbrot law on musical pieces encoded in MIDI. Our hypothesis is that this will allow us to computationally identify and emphasize aesthetic aspects of music. Specifically, we have identified an initial set of attributes (metrics) of music pieces on which to apply the Zipf-Mandelbrot law. These metrics include pitch of musical events, duration of musical events, the combination of pitch and duration of musical events, and several others. Our results are encouraging. We are working on automating our metrics so that we can test our hypothesis on a wide variety of music genres (baroque, classical, 20, century, blues, jazz, etc. ). If this project is successful, we plan to investigate how such metrics may be used in various areas such as music education, music therapy, music recognition by computers, and computer-aided music analysis/composition.
Full-text available
We discuss the application of the Zipf-Mandelbrot law to musical pieces encoded in MIDI. Specifically, we have identified a set of metrics on which to apply this law. These metrics include pitch of musical events, duration of musical events, the combination of pitch and duration of musical events, and several others. Our hypothesis is that these metrics will allow us to computationally identify and perhaps emphasize aesthetic aspects of music. We have developed a system that automates calculation of these metrics in MIDI-encoded pieces. Using this tool we conducted a study on a corpus of 220 pieces from baroque, classical, romantic, 12-tone, jazz, rock, DNA strings, and random music. Our results support the above hypothesis. We discuss limitations, and how to minimize the potential for statistical error through composite metrics. We present possible applications and an overview of preliminary work in computer-aided music composition.
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In Faster Than Thought, B. V. Bowden (ed.), London: Pitman, 1953.
An abstract is not available.
This paper is concerned with the problem of constructing a computing routine or “program” for a modern general purpose computer which will enable it to play chess. Although perhaps of no practical importance, the question is of theoretical interest, and it is hoped that a satisfactory solution of this problem will act as a wedge in attacking other problems of a similar nature and of greater significance. Some possibilities in this direction are:- (1) Machines for designing filters, equalizers, etc. (2) Machines for designing relay and switching circuits. (3) Machines which will handle routing of telephone calls based on the individual circumstances rather than by fixed patterns. (4) Machines for performing symbolic (non-numerical) mathematical operations. (5) Machines capable of translating from one language to another. (6) Machines for making strategic decisions in simplified military operations. (7) Machines capable of orchestrating a melody. (8) Machines capable of logical deduction.