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An Innovative Employment of Virtual Humans to Explore the Chess Personalities of Garry Kasparov and Other Class-A Players

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Exploring chess players of different personalities, including the strengths and weaknesses of each remains an essential component in designing new chess applications. Research shows that virtual players play an essential role in helping researchers to explore chess personalities of different classes and playing styles. A virtual chess player is defined as a software simulation that mimics the playing style of a real chess player. The current study employs these players in investigating the personalities of three class-A players while competing against Garry Kasparov. Additionally, it examines the personality of Kasparov and how he performs while competing against the other class-A players. To this end, the study utilizes an experimental design to collect data from simulations of games between three class-A players against Kasparov. The class-A players range in their personalities: a player who prefers chess material, drawish, and a balanced player. The four players in the simulation are virtual humans that are programmed to represent real chess players. The findings reveal that the class-A chess players did not have the same performance. Likewise, the performance of Kasparov varied according to the opponent, although his opponents were from the same category.
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An Innovative Employment of Virtual Humans
to Explore the Chess Personalities of Garry
Kasparov and Other Class-A Players
Khaldoon Dhou
Drury University, Springfield, MO
Abstract. Exploring chess players of different personalities, including
the strengths and weaknesses of each remains an essential component
in designing new chess applications. Research shows that virtual players
play an essential role in helping researchers to explore chess personalities
of different classes and playing styles. A virtual chess player is defined as
a software simulation that mimics the playing style of a real chess player.
The current study employs these players in investigating the personal-
ities of three class-A players while competing against Garry Kasparov.
Additionally, it examines the personality of Kasparov and how he per-
forms while competing against the other class-A players. To this end, the
study utilizes an experimental design to collect data from simulations
of games between three class-A players against Kasparov. The class-A
players range in their personalities: a player who prefers chess material,
drawish, and a balanced player. The four players in the simulation are
virtual humans that are programmed to represent real chess players. The
findings reveal that the class-A chess players did not have the same per-
formance. Likewise, the performance of Kasparov varied according to the
opponent, although his opponents were from the same category.
Keywords: games, chess, personality, virtual humans, chess software,
grandmasters, Kasparov
1 Introduction
Computer chess has attracted much attention over the years and it has
been subjected to an extensive investigation by researchers from various
disciplines. Recent years witnessed significant developments including
computers and chess programs that are able to compete at a very high
level and defeat top grandmasters. Nowadays, chess technology is very
affordable and is widely used in training. Additionally, it offers many
services, including the employment of virtual humans to enable chess
players to train and compete against players of different skills and playing
styles. For the purpose of this research, virtual chess humans are defined
as software simulations that mimic real players including many world
champions such as Kasparov and Polgar. These virtual humans make it
flexible for a chess player to explore the playing styles of many other real
players, ranging from beginners to top-rank grandmasters.
2 Khaldoon Dhou
Each chess player, including virtual players, is described by two at-
tributes: chess rating and chess personality. Chess rating is a numerical
value assigned to each player depending on how he performs versus other
opponents in the chess community — the higher the rating, the stronger
the player [27,30]. On the other hand, chess personality is a term utilized
in previous HCI research, and it is defined as the perspective of a player
during his chess games against other players of different styles [22]. For
example, Kasparov’s personality is characterized by his ability to make
rapid calculations and explore innovative opening styles, which are the
result of his extensive development [52]. He often offers piece sacrifices in
order to allow his pieces to have extra flexibility to move over the chess-
board. Kasparov was the world champion for more than twenty years,
and he is considered the greatest player in history. This study investigates
the personality of Kasparov by analyzing the games between Kasparov
and three class-A players: Rand, Dobie, and Sunny. The three players
vary in their chess personalities: Dobie is a player who prefers chess ma-
terial, Sunny is a player who considers drawing games at an early stage,
and Rand is a balanced player. The description of all the players em-
ployed in this research, including Kasparov, is offered by Ubisoft [52].
Additionally, the study examines the influences of Kasparov’s personal-
ity on different class-A players. This study uses three measurements for
chess personalities: number of moves in a game, error of a chess player,
and the Chessmaster agreement percentage. All these measurements are
obtained from analyzing the games in the study using the Ubisoft Chess-
master software.
The reason of choosing Kasparov in this study is that there is a
growing body of literature that recognizes the importance of exploring
his personality in chess [26, 28, 33, 37, 54]. His personality can play a sig-
nificant role in addressing many issues in designing chess programs and
understanding chess psychology. That is to say, analyzing the games
between Kasparov and Deep Blue is still a primary concern for many
researchers [11, 16, 38, 40, 51]. To the best of the researcher’s knowledge,
this is the first article exploring the personality of Kasparov while com-
peting against other class-A players by the utilization of virtual humans.
Additionally, the extensive literature review reveals only one study that
explores chess personalities via the involvement of virtual humans [22].
Using virtual players to help in understanding chess personalities is cru-
cial because of many reasons: It makes it easier for psychologists to use
virtual humans as a tool to understand many aspects of games and their
outcomes. Additionally, it can be used as a tool for helping medical stu-
dents identify particular training techniques as research indicates the
employment of tools with chess personalities in surgical training [47].
This research investigates the following questions:
How does Kasparov perform against different class-A players who
vary in their chess personalities?
Is Kasparov making less or more errors when the personality of his
opponent changes?
How do the personalities of class-A opponents playing against Kas-
parov affect the length of the game?
Employing Virtual Humans to Explore Competition Against Kasparov 3
Do class-A players of various personalities perform differently while
competing against Kasparov?
The remaining part of the paper proceeds as follows: Section 2 pro-
vides an overview of the related research in virtual humans, chess psy-
chology, and personalities in HCI research; Section 3 describes the ap-
proach employed in this study; Section 4 outlines the results obtained
from analyzing the games between the four virtual players; the findings
are discussed in Section 5; Section 6 concludes the paper.
2 Related work
Virtual humans are increasingly playing vital roles in our daily lives.
They are the outcome of the union of various disciplines such as psy-
chology, human-computer interaction, gaming, and artificial intelligence.
Researchers design them to serve in different domains such as medicine,
tourism, instruction, and entertainment. Personality is an essential as-
pect of virtual humans. It has been a subject of many research studies
exploring virtual humans in psychology and computer science. For exam-
ple, Zibrek et al. [57] administered a research study to collect information
on how virtual figures are perceived in virtual reality applications and
whether their personality makes a difference or not. Their major finding
was that the closeness towards virtual figures is described as a compos-
ite interplay between the appearance and personality. Similarly, Zhou et
al. [56] presented virtual interviewers that communicate with users and
judge their personality characteristics. Their study reported that the per-
sonality traits and interview context influence people to place trust in
virtual humans.
Evidence from the literature suggests that chess is an attractive field
to explore many questions about personalities, people, and societies [22,
34, 47]. A recent study by Dhou [22] explored different aspects of chess
personalities from the perspective of virtual humans. In his work, he in-
vestigated the personalities of different virtual chess players and linked
the findings to existing research in social sciences. Interestingly, the study
shows that virtual chess players with identical ratings and different per-
sonalities can perform differently depending on their opponent. Addi-
tionally, he found that a grandmaster with an attacking style stimulates
other less skilled players and causes them to make fewer mistakes as op-
posed to when they compete against a defensive grandmaster. This find-
ing has roots in psychology, where people attempt to comprehend the
difficult events in their lives when they take place [55]. What remains
unknown is researching more chess personalities and how they are influ-
enced by each other. Although it is possible to explore chess personalities
by investigating real players, virtual chess players give a much greater
flexibility in pairing players with different personalities, including world
champions against less skilled players. Such flexibility is impractical, if
not impossible with real human players.
Several attempts have been made to investigate the personalities of
chess players. A classical work was conducted by de Groot [17] who exam-
ined players of different levels and attempted to explore the variations
4 Khaldoon Dhou
between experts and beginners. De Groot observed that chess experts
could recall and reestablish meaningful chess patterns over the board as
opposed to weaker players. Similarly, Chase and Simon [13] discovered
that experts have faster recognition of chess patterns than chess be-
ginners. Later, Vollst¨adt-Klein et al. [53] examined the personalities of
advanced chess players and how they can affect chess performance. They
found that female chess players were happier and had higher accomplish-
ments than other females. On the other hand, their study reported that
there was not a significant difference between the personality profiles of
male players and non-players. Likewise, Stafford [49] employed an exten-
sive database of games and discovered that female chess players exceed
the expectations when they play against male chess players. For more
studies investigating chess and gender, the reader is referred to [7, 9, 31,
32]. Dhou [19] classified chess applications into different categories and
identified the best training approaches in each. Bilali´c et al. [6] explored
the personalities of children who play chess and their companions who do
not. Their study revealed that children who scored higher in particular
tests are more likely attracted to chess than their peers. Blanch [8] ex-
amined the top one hundred world champions and employed the domain
latent curve model to investigate the personal differences. They found a
strong association between age and tournament activity. Together these
studies provide important insights into the psychology of chess players.
Although all these previous attempts investigated the personalities
of chess players and how they perform in different settings, the topic of
virtual chess players has still not yet been formally studied. The exten-
sive literature review revealed that there is only one study that explored
the personalities of virtual chess players [22]. The main advantage of em-
ploying virtual humans over real human players in understanding chess
personalities is the flexibility in allowing players from different eras to
compete against each other. For example, Dhou [22] investigated the
variations between Leko and Anderssen who are grandmasters that exist
in different periods. Another advantage of utilizing virtual players lies in
the flexibility of designing a controlled experiment between a wide range
of players of different skills. Interestingly, research showed that there is
a strong correlation between certain moves made by humans and chess
computers [36].
It is essential to note that current research recognizes the critical
role played by personalities in HCI research. For example, Shohieb [48]
developed a game that teaches children how to manage different disaster
situations. Additionally, Sarsam and Al-Samarraie [46] introduced a user
interface based on personality traits for mobile applications. Other stud-
ies investigated the issue of connecting the personality traits to the visual
design favorites of users [1,2, 45]. Caci et al. [12] explored the motives
of Pok´emon Game practice, personal variations linked with individual
characteristics, and game attitudes. Bacos et al. [5] explored the influ-
ence of different personality traits on in-game personality demonstrative
of counterfactual thinking. They found that personality relies on play-
ers’ variations and their experiences of the game itself. In another study,
McCreery and Krach [39] investigated the causes of why people appear
aggressive in an online setting and explored different types of aggression.
Employing Virtual Humans to Explore Competition Against Kasparov 5
They found that proactive aggression was prophesied via agreeableness,
extraversion, and emotional stability, while the reactive aggression was
prognosticated via agreeableness and emotional stability. More research
explored online learning environments and the students’ feedback [4, 15].
The investigation of creatures’ behavior is not limited to humans. It in-
cludes the behaviors of other creatures in different virtual environments
such as biological reproduction, ants, and ecological systems [3, 18, 21,
23, 41]. Many of these studies are aimed at reducing the size of binary
data that is widely used in text and other formats [20, 24, 25, 44].
To summarize, although psychologists and computer scientists have
frequently emphasized virtual humans in different applications, there is
only one study investigating their role in understanding chess personali-
ties [22]. This article investigates the personalities of Kasparov and three
class-A players to explore how a player from a particular class can be
influenced when he competes against a player from another class.
3 Method
3.1 Participants
This study investigates four virtual chess players: three class-A players
and Kasparov. The class-A players have different chess personalities, as
follows:
Dobie: He somewhat goes for chess material while competing against
other players.
Rand: He is a balanced chess player and characterized by a profound
proficiency in chess openings.
Sunny: She is not competitive and attempts to draw her games from
the beginning. Additionally, she is known for controlling the center,
but sometimes ignores the pawn structure.
The three class-A players have almost identical USCF ratings. The USCF
ratings of Dobie, Rand, and Sunny are 2118, 2113, and 2115, respectively.
In this study, the three class-A players play against Garry Kasparov,
who makes rapid calculations and considers creative openings. In addi-
tion, Kasparov sometimes chooses neglected opening styles such as Evans
Gambit. It is important to emphasize that the four players employed in
the current study are virtual humans that mimic real chess players.
3.2 Materials
The present design involves two independent variables:
IV1: The color of Kasparov’s pieces. In this study, each opponent
played half of the games with White and the other half with Black.
IV2: The personality of Kasparov’s class-A opponent. This inde-
pendent variable has three levels: a player who prefers material, a
drawish, and a balanced player.
The researcher utilized the Chessmaster developed by Ubisoft to an-
alyze all the chess games in the study [52]. To this end, the researcher
considers measurements of five dependent variables generated by the
Chessmaster, as follows:
6 Khaldoon Dhou
DV1: The total number of moves
DV2: The total error of moves played by Kasparov
DV3: The total error of moves played by a class-A player
DV4: The Chessmaster’s agreement percentage of Kasparov’s moves
DV5: The Chessmaster’s agreement percentage of a class-A player’s
moves
The total error is a metric employed in calculating the errors made
by different virtual players. It is calculated as the difference between the
actual moves made by players and the optimal moves [14,22]. The same
metric was previously used in exploring virtual humans to understand
the differences between chess personalities [22].
3.3 Procedure
In this research study, each class-A player played 98 games against Kas-
parov, half of them with white, and the other half with black. The re-
searcher collected the data from all the games and analyzed it using the
Chessmaster. The Chessmaster generated the five dependent variables
for each game. The researcher used these dependent variables in explor-
ing the personalities of the four virtual chess players employed in this
study.
4 Results
The researcher analyzed the data in this study using a series of two-way
ANOVA tests. Each dependent variable was submitted to a two color of
Kasparov (White or Black) by three class-A player personality (drawish,
prefers material, and balanced) two-way ANOVA. All the effects were
reported as significant at p < 0.05.
4.1 Number of moves
The researcher conducted a series of two-way ANOVA tests to exam-
ine the effect of the class-A player’s personality and Kasparov’s color
on each of the five dependent variables. There was a significant main
effect of the class-A player, on the number of moves during the games,
F(2,288) = 6.686, p= 0.001. Paired samples t-tests show that there
are statistically significant differences between the number of moves in
the games played by different class-A players against Kasparov. There
was a significant difference in the number of moves played by Rand
(M= 52.459, SD = 10.429) and the number of moves played by Sunny
(M= 58.888, SD = 17.684); t(97) = 2.844, p= 0.005. Similarly, there
was a significant difference in the number of moves played by Sunny
(M= 58.888, SD = 17.684) and the number of moves played by Dobbie
(M= 51.622, SD = 16.358); t(97) = 2.968, p= 0.004.
Employing Virtual Humans to Explore Competition Against Kasparov 7
4.2 Kasparov’s total error
There was a significant main effect of the class-A player, on the total
error of Kasparov during his games, F(2,288) = 3.108, p= 0.046. A
paired samples t-test reveals a significant difference in the total error of
Kasparov when he competes against Sunny (M= 2.245, SD = 3.197)
and when he competes against Dobbie (M= 1.290, SD = 2.412); t(97) =
2.349, p= 0.021.
4.3 Class-A player’s total error
There are no significant effects.
4.4 Chessmaster’s agreement percentage on Kasparov’s
moves
The interaction between Kasparov’s color and the player is significant,
F(2,288) = 3.262, p= 0.04 (Figure 1). To break down this interac-
tion, the researcher conducted a series of paired samples t-tests. Paired
samples t-tests show that when Kasparov plays with Black, there are sig-
nificant differences between the Chessmaster’s agreement percentages on
his moves when he competes against Dobie (M= 97.204, SD = 2.041)
and Rand (M= 96.081, SD = 3.054); t(48) = 2.092, p= 0.042; and
when he competes against Sunny (M= 95.694, SD = 2.823) and Dob-
bie (M= 97.204, SD = 2.041); t(48) = 2.931, p= 0.005.
4.5 Chessmaster’s agreement percentage on class-A
players’ moves
There was a significant main effect of the class-A player on the chess-
master’s agreement percentage on the moves made by class-A players,
F(2,288) = 7.791, p= 0.001. Further paired samples t-tests show that
on average, the Chessmaster agrees more on the moves made by Sunny
(M= 88.704, SD = 4.878) than Rand (M= 86.418, S D = 4.957);
t(97) = 3.143, p= 0.002, and on the moves made by Sunny (M= 88.704,
SD = 4.878) than Dobie (M= 85.939, S D = 5.779); t(97) = 3.628,
p < 0.001.
5 General discussion
The purpose of the current research study was to explore the psychology
of competition between Kasparov and three class-A players. To this end,
the researcher designed a study consisting of four virtual chess players:
Kasparov and three other class-A players. In the current experiment,
the researcher examined different dependent variables that measure the
lengths of the games and the performance of the involved virtual players.
The experimental results showed that Kasparov tends to make more
mistakes when he plays against Sunny (drawish) as opposed to play-
ing against Dobie (prefers material). These findings are consistent with
8 Khaldoon Dhou
Black
White
GM Color
Fig. 1. The mean values of the Chessmaster’s agreement percentage on Kasparov’s
moves. Except for Dobie, the Chessmaster agrees more on Kasparov’s moves when he
plays with the White color.
the outcomes from the previous study [22] investigating the errors made
by grandmasters while playing against other class-B players. The study
in [22] showed that grandmasters performed differently while playing
against different players from the same class. A possible explanation is
that Sunny has a good control of the center of the game. That is to
say, although she neglects the pawn structure, Kasparov’s total error
was higher when he competes with her as opposed to competing with
Dobie. Her strength is evidenced by the chess literature revealing that
controlling the center is more important than having effective pawn com-
binations [35]. Additionally, one of the standard powerful fundamental
postulates in chess is that a strong side attack requires a solid center,
which increases the chances of attack [10]. Kasparov did better when
Sunny accepted the Queen’s Gambit (Figure 2). The variation of accept-
ing the Queen’s Gambit sounds like a favorite direction for Kasparov,
and that is probably why he performed better as opposed to the other
variation of declining the Gambit. It is essential to mention that Kas-
parov does well in the opening phase and his game against Deep Blue
reveals that the computer could not outplay him during the opening [42].
Controlling the center did not only influence the total error of Kas-
parov, but it also affected the moves in the games. The paired samples
t-tests showed that Sunny was the most resisting player, and the games
Employing Virtual Humans to Explore Competition Against Kasparov 9
(a)
(b) (c)
Fig. 2. An example showing two variations of the Queen’s Gambit. In the two games,
Kasparov plays with White and Sunny plays with Black. In (a) Kasparov offers a
pawn sacrifice; (b) Sunny decides to choose the accepted Gambit variation that allows
Kasparov to control the center; In (b) Sunny decides to decline the Gambit. The results
from two games showed that Kasparov made fewer errors and took a greater advantage
when Sunny accepted the Queen’s Gambit
against her were the most extended in the simulation. Additionally, an-
alyzing two games showed that giving up the center by accepting the
Queen’s Gambit allowed Kasparov to gain more advantage as opposed
to Sunny declining the Gambit. More research is needed to investigate the
effects of declining the Gambit. Interestingly, previous research shows a
strong relationship between the chess center principle and management.
Flamholtz [29] used this principle in his analogy, describing centralized
and decentralized management. Another explanation of why Kasparov
did better when Sunny accepted the Gambit is probably because of the
opening style. In other words, the opening phase determines the direc-
tion of the game, and each player has his preferences. For example, when
Sunny accepted the Queen’s Gambit in one game, the total error of Kas-
parov was 0, while it was 6.04 when she declined the Gambit. The pawn
10 Khaldoon Dhou
sacrifice is often offered by Kasparov so that he can get additional mo-
bility to his chess pieces.
The results showing that Kasparov did better when Sunny accepted
the Queen’s Gambit are consistent with other findings revealing the im-
portance of the opening phase. The opening determines the flow of the
game and might even cause a player to lose. For instance, Deep Thought
defeated Karpov in the opening and the initial stage of the middle game
and had many circumstances to draw the game [43, p. 197]. Different
grandamsters and research studies have noted the importance of open-
ings in chess. For example, Michael Adams emphasizes the importance
of the opening and believes that working on it is more applicable than
working on other phases of the game [50]. Interestingly, in the previous
study, the findings showed that a player who is good at the opening did
better than a balanced player, although they belong to the same cate-
gory [22]. Additionally, Levene [36] showed the importance of the opening
books as part of chess engines. Chess applications are connected to large
databases that contain different openings and their variations.
6 Conclusion
The present study was designed to determine the effect of the personality
of chess players on the outcomes of their games against different oppo-
nents. To this end, the study involves designing an experiment consisting
of four virtual chess players: one grandmaster, and three class-A players.
The selected virtual grandmaster was Garry Kasparov, and the three
class-A players varied in their personalities. One of the more significant
findings to emerge from this study is that Kasparov performed differently
while competing with the other class-A players. Additionally, Kasparov
did better when the other player followed his line of play (i.e., Accepted
Queen’s Gambit). Similarly, the three class-A players performed differ-
ently although they had the same opponent, Kasparov.
These findings suggest that in general players can behave differently
depending on their opponent even if they are within the same class.
Additionally, the present findings are consistent with the previous out-
comes in [22], showing the differences between players from the same class
with different personalities. Furthermore, class-A players performed dif-
ferently when they were competing with Kasparov, although they have
almost identical ratings. The outcomes from this research can help under-
stand the ratings of chess games between players of different personalities
and ratings. That is to say, this study paves the way for further research
that explores the influence of different chess personalities on each other
to investigate new techniques for chess training based on opponents. For
example, in the study in [22], the findings showed that less skilled players
performed better while competing against an aggressive grandmaster as
opposed to a defensive player. Similarly, the current study revealed that
the software agrees more on the moves made by a player who controls
the center of the game. Such findings reinforce the chess concept that
stresses on the importance of controlling the center. Additionally, they
can be used as guidelines for chess players to recommend individual per-
sonalities for chess training, showing that chess rating is not the only
Employing Virtual Humans to Explore Competition Against Kasparov 11
factor to select an opponent for training and personality is also another
significant factor.
This research has many practical applications. For example, it helps
in designing new chess programs that take the chess personality into
consideration and suggest opponents depending on the personality of
the player. In other words, some players perform better while competing
against certain players, and it would help to suggest different opponents
from which they can probably learn the most. Second, further under-
standing of the chess personalities allows designing new experiments that
can probably reveal new findings about social aspects. For example, the
study in [22] revealed many interesting findings that are linked to social
sciences. In general, therefore, it seems that virtual chess players can
contribute to social sciences and prove useful in understanding human
behavior.
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