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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
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
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
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
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
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
(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
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... For instance, the challenge for AR game developers is to assign users a more dynamic and autonomous role in their gaming experiences [41], so that people might assign the highest perceived value to this kind of games [42]. As well, AI research that developed artificial agents able to handily beat a human being at the classic board game, just as IBM's DeepBlue system bested Russian grandmaster Garry Kasparov back in 1997, has accelerated in recent years [21]. ...
... A similar study in this area is the work explored the personality of Kasparov by using virtual humans to simulate Kasparov and three proposed opponents [21]. The importance of this work is that it offers an understanding of Kasparov's personality, which has attracted much attention among psychologists and artificial intelligence researchers. ...
... That is to say, there is evidence that the personality of Kasparov plays a crucial role in designing chess-playing programs [31,51]. Additionally, recent research shows the psychology of competition between different opponents against Kasparov and the differences in their performances according to their chess personalities [21]. Together the three studies in virtual chess humans [19,21,22] provide important insights into chess personalities and using them in analyzing chess outcomes. ...
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... Previous studies that examine the correlation between users' personality and their eye-movements indicate that people with similar personality traits tend to exhibit similar eye-movements [17]. Personality traits are indicators of individuals' tendencies from behavioural, cognitive and emotional perspectives [18]. Differences in personality can be examined based on the Big-Five model of personality (also known as the five-factor model) that is used extensively in psychology [19]. ...
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... Interestingly, many of these agent-based modeling techniques work within virtual environments. Existing studies show that virtual environments brought significant contributions to the research community including helping researchers examine many aspects of human personalities [40][41][42][43][44]. ...
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... Of course between human players (even grandmasters), the moves actually played from a given position may not always be the best. In fact, the moves are often affected by their personalities and playing styles [20] which is what also makes them interesting to people and worth recording. ...
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We present an algorithm that correctly updates the Forsyth-Edwards Notation (FEN) chessboard character string after any move is made without the need for an intermediary array representation of the board. In particular, this relates to software that have to do with chess, certain chess variants and possibly even similar board games with comparable position representation. Even when performance may be equal or inferior to using arrays, the algorithm still provides an accurate and viable alternative to accomplishing the same thing, or when there may be a need for additional or side processing in conjunction with arrays. Furthermore, the end result (i.e. an updated FEN string) is immediately ready for export to any other internal module or external program, unlike with an intermediary array which needs to be first converted into a FEN string for export purposes. The algorithm is especially useful when there are no existing array-based modules to represent a visual board as it can do without them entirely. We provide examples that demonstrate the correctness of the algorithm given a variety of positions involving castling, en passant and pawn promotion.
The continuous changes in the size of data create new challenges to design new techniques to reduce its size and encode it in a way that changes its original representation. In this article, we develop a bat-bug agent-based modeling simulation for chain coding and employ it in compressing bi-level image information. The system consists of agents that are classified into static and dynamic depending on their movements. Bugs are considered static agents, and they are distributed over the virtual environment according to the allocation of pixels in the original image. On the other hand, bats are dynamic agent, and their role is to move around to consume bugs while the algorithm tracks their movements. Bats are designed in a way to move within certain boundaries to avoid crashing into each other. Bats employ specific movements that allow them to move in relative directions. Therefore, the frequency of their movements can follow a certain pattern that can help in further size reduction. In other words, the integration of relative movements into our design proved to be advantageous because there is an observable pattern of repeated movements, which allows getting higher compression results. Finally, arithmetic coding is applied to the final strings that represent the movements of bats while searching for bugs to eat. To assess the performance of the algorithm, we compared the findings against standardized benchmarks used in the image processing community: G3, G4, JBIG1, and JBIG2. The outcomes show that we could outperform all these benchmarks using all the images we used for testing. Additionally, we conducted a series of paired samples t-tests, and they revealed that the mean differences between our results and those obtained from other benchmarks are statistically significant.
In this paper, we develop a new chain coding application stimulated by the existing NetLogo HIV agent-based model and utilize it in bi-level image compression. Our paper is an extended version of our previously published paper in the International Conference on Computational Science 2021. Our method considers converting an image into a virtual environment, which maps to the original image and consists of HIV+ and HIV- female agents depending on the distribution of the pixels. Then, the algorithm introduces HIV+ male agents the purpose of which is to move around and infect other HIV- female agents. The movements of the HIV+ male agents are designed in a way that follows the relative coding approach, utilized in different chain coding projects. The relative coding increases the likelihood of generating consecutive codes that are encoded in a similar manner, and therefore, helps in providing better compression ratios. The algorithm tracks certain HIV+ male movements and uses them along with other pieces of information to reconstruct the original image back. As the literature shows, agent-based modeling can be advantageous over mathematical techniques and it can be effectively applied in some domains. The outcomes revealed that we could outperform standardized benchmarks used by researchers in the image processing community. Additionally, the paired samples t-tests reveal that the mean differences between our results and the ones generated by the other benchmarks (e.g. JBIG2) are statistically significant.
The topic of virtual humans is increasingly important in the field of Artificial Intelligence. Avirtual human can be defined as a computer simulation that mimics an actual human. Virtual humans are widely used in gaming, business, and many other domains. This paper presents an experiment that utilizes virtual chess players that simulate real players to examine the chess personalities of four chess players: a grandmaster and three class-A players. The selected grandmaster is Chigorin who is characterized by his fear of attack strategy. On the other hand, the class-A players represent three chess personalities: negligence of the center and capturing more than usual pieces, employment of traps in the opening phase, and solid openings and control of the center. To this end, the experiment represents simulations of games between Chigorin and each of the three class-A players. The errors of Chigorin and class-A players were utilized as dependent variables to measure the performance of the players. The findings reveal that although the class-A players have almost similar chess ratings, they performed differently during the simulations. Likewise, the outcomes showed that Chigorin played differently while competing against each of the class-A opponents. The results indicate that chess personalities play a significant role in predicting game outcomes between different players and it should be used as a factor along with the chess ratings.
In this paper, we utilize the NetLogo HIV model in constructing an environment for bi-level image encoding and employ it in compression. Our model considers converting an image into a virtual environment that comprises female agents testing positive and negative for HIV. Female agents are scattered according to the allocation of the pixels in the original images to be tested. The simulation considers introducing male agents that test positive for HIV, the purpose of which is to track their movements while infecting other HIV- female agents. The progressions of the HIV+ male agents within the simulation take advantage of the relative encoding approach previously used by other image processing and agent-based modeling researchers. That is to say, the simulation allows generating a high proportion of similar movement forms that are similarly encoded regardless of the movements of agents. This is followed up by applying Huffman coding to the obtained chains of movement strings for further reduction. The ultimate results reveal that our product could outperform existing benchmarks using all the images we employed in testing.
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We present artificial intelligent (AI) agents that act as interviewers to engage with a user in a text-based conversation and automatically infer the user's personality traits. We investigate how the personality of an AI interviewer and the inferred personality of a user influences the user's trust in the AI interviewer from two perspectives: the user's willingness to confide in and listen to an AI interviewer. We have developed two AI interviewers with distinct personalities and deployed them in a series of real-world events. We present findings from four such deployments involving 1,280 users, including 606 actual job applicants. Notably, users are more willing to confide in and listen to an AI interviewer with a serious, assertive personality in a high-stakes job interview. Moreover, users’ personality traits, inferred from their chat text, along with interview context, influence their perception of and their willingness to confide in and listen to an AI interviewer. Finally, we discuss the design implications of our work on building hyper-personalized, intelligent agents.
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The paper compares two key-events that marked the narratives around the emergence of AI in two different time frames: the game series between the Russian world champion Garry Kasparov and the IBM supercomputer Deep Blue held in New York in 1997; and the GO game series between the South Korean champion Lee Sedol and DeepMind's artificial intelligence AlphaGo held in Seoul in 2016. Relying on a corpus of primary and secondary sources such as newspapers and specialized magazines, biographic books, the live broadcasts and the main documentaries reporting the challenges, the paper investigates the way in which IBM and Google DeepMind used the human-machine competition to narrate the emergence of a new, deeper, form of AI. On the one hand, the Kasparov-Deep Blue match was presented by broadcasting media and IBM itself as a conflictual and competitive form of struggle between human kind and a hardware-based, obscure and humanlike player. While on the other hand, the social and symbolic message promoted by DeepMind and the media conveyed a cooperative and fruitful interaction with a new software-based, transparent and un-humanlike form of AI. The analysis of the case studies reveals how AI companies mix narrative tropes, gaming and spectacle in order to promote the newness and the main features of their products. In particular, recent narratives of AI based on human feelings and values such as beauty and trust can shape the way in which the presence of intelligent systems is accepted and integrated in everyday life.
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Aesthetics or beauty in chess is a quality appreciated by most players. However, there is scant research on the differences of aesthetic perception between the genders, especially given the lower participation of females in this domain. Using an experimentally-validated computational aesthetics model for chess, we evaluated a fair selection of winning chess move sequences taken from games played between women and men. Contrary to previous research that was not as thorough, we found no statistically significant difference in the aesthetic quality of those sequences between the groups. The results suggest that aesthetic ability, perception and appreciation in the game are likely not affected by gender. This also implies that training methods and promotion of the game to girls or young women have less, if any, basis for being any different from those that pertain to boys or men. Furthermore, the arguably absolute lack of participation of women in the sub-domain of chess problem composition - in which aesthetics plays an even more significant role - likely has little, if anything, to do with innate capability unless otherwise demonstrated.
In this article, the researcher introduces a hybrid chain code for shape encoding, as well as lossless and lossy bi-level image compression. The lossless and lossy mechanisms primarily depend on agent movements in a virtual world and are inspired by many agent-based models, including the Paths model, the Bacteria Food Hunt model, the Kermack–McKendrick model, and the Ant Colony model. These models influence the present technique in three main ways: the movements of agents in a virtual world, the directions of movements, and the paths where agents walk. The agent movements are designed, tracked, and analyzed to take advantage of the arithmetic coding algorithm used to compress the series of movements encountered by the agents in the system. For the lossless mechanism, seven movements are designed to capture all the possible directions of an agent and to provide more space savings after being encoded using the arithmetic coding method. The lossy mechanism incorporates the seven movements in the lossless algorithm along with extra modes, which allow certain agent movements to provide further reduction. Additionally, two extra movements that lead to more substitutions are employed in the lossless and lossy mechanisms. The empirical outcomes show that the present approach for bi-level image compression is robust and that compression ratios are much higher than those obtained by other methods, including JBIG1 and JBIG2, which are international standards in bi-level image compression. Additionally, a series of paired-samples t-tests reveals that the differences between the current algorithms’ results and the outcomes from all the other approaches are statistically significant.
The accelerated progress of the Internet of Things (IoT) and the nature of its heterogeneous parts cause difficulties in transferring data between different components. These rapid developments make it crucial to investigate new techniques to reduce the size of the data transmitted over the channels. In this article, we develop an original chain code for compression that is inspired by the concept of biological reproduction. The new chain code is implemented via developing an agent-based modeling simulation consisting of rabbits, carrots, and paths for the rabbits to wander. The environment in the model represents an actual bi-level image consisting of zeros and ones. In our ‘biological reproduction’ method, a rabbit starts wandering in the virtual world to consume carrots, while its movements are tracked and recorded by the algorithm. Each rabbit’s movement is recorded based on its previous movement. Additionally, after a rabbit consumes a certain number of carrots, it gains energy and therefore reproduces another rabbit, which continues to work on the same image, and so on. Accordingly, more rabbits simultaneously work on different parts of an image, which makes the ‘biological reproduction’ method advantageous over many other classical image processing approaches. The experimental results showed that the current method could outperform well-known image compression techniques, including JBIG family algorithms, on all the images used for testing.
Artificial intelligence (AI) is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies, such as graphic processing unit, Internet of Things, cloud computing, and the blockchain, in the new generation of big data and Industry 4.0. In this paper, we construct an extensive survey over the period 1961–2018 of AI and deep learning. The research provides a valuable reference for researchers and practitioners through the multi-angle systematic analysis of AI, from underlying mechanisms to practical applications, from fundamental algorithms to industrial achievements, from current status to future trends. Although there exist many issues toward AI, it is undoubtful that AI has become an innovative and revolutionary assistant in a wide range of applications and fields.
The domain of chess has served in the past to address the individual variability in expert development and performance. However, the process towards expertise has been generally unexplored concerning top experts at the extreme upper tail of the chess skill distribution. This study analyzed the top hundred chess players in the world from 2009 to 2015. A cross-domain latent curve model (LCM) was used to examine individual differences in age and in the simultaneous trajectory in chess tournament activity and chess performance. The main findings indicated individual differences in the change in tournament activity and performance, albeit tournament activity decreased whereas performance increased. Younger players were more involved in tournament activity and increased their performance at a faster pace than older players did. The dynamic change in tournament activity was a stronger predictor of the change in performance than the static cumulated amount of activity. Age and the change in tournament activity explained only half of the variation in the change in performance across the observed period.
This article reports a study exploring motivations of Pokémon Game use, individual differences related to personality traits, and game habits. First, it analyzed Pokémon GO motivations through exploratory factor analysis (EFA) by administering online the Pokémon GO Motivational Scale to a group of Italian gamers (N = 560). Successively, a Confirmatory Factor Analysis (CFA) was conducted testing three factorial models of Pokémon Game motivations on a selected random sample (N = 310). Results showed a three-factor model of Pokémon GO Game motivations (i.e. Personal Needs, Social Needs and Recreation), accounting for 68.9% of total variance plus a general higher order factor that best fits the data. Individual differences in Pokémon GO motivations and personality traits have been explored showing that high involved Pokémon GO players are introverted, low agreeableness, and conscientiousness people, driven by personal social and recreational needs. Reciprocal influences on motivational involvement, personality, and game habits were discussed.