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A Novel Investigation of Attack Strategies via the Involvement of Virtual Humans: A User Study of Josh Waitzkin, a Virtual Chess Grandmaster



A growing body of evidence suggests that attack is a significant concept that has been explored by researchers from various disciplines such as marketing, psychology, and computing. Additionally, there has been substantial research undertaken on the role of attack in chess, which brought significant contributions to different fields of research. In this paper, the researcher investigates the attack concept in chess, as a strategic game by exploring virtual chess players of different strategies. In particular, the researcher explores the performance of an attacking grandmaster against three other class-A players of different chess personalities that vary in controlling the center of the chessboard. To this end, the researcher collected data from four virtual chess players: a grandmas-ter and three class-A players. The selected grandmaster is Josh Waitzkin who is known for his fearless attacking style and deep endgame understanding. The class-A players have different personalities: (1) a player who strongly controls the center of the board; (2) a player who ignores the center; and (3) a player who offers traps to control his opponent. The researcher measured different dependent variables including the errors of the players and the moves of the games. The findings show that class-A players of particular chess personalities perform differently. Additionally , the study reveals that there is a positive relationship between the findings and some of the existing real-life scenarios in the business domain.
A novel investigation of attack strategies via the
involvement of virtual humans: A user study of
Josh Waitzkin, a virtual chess grandmaster
Khaldoon Dhou
College of Business Administration
Texas A&M University-Central Texas
Abstract. A growing body of evidence suggests that attack is a signif-
icant concept that has been explored by researchers from various disci-
plines such as marketing, psychology, and computing. Additionally, there
has been substantial research undertaken on the role of attack in chess,
which brought significant contributions to different fields of research. In
this paper, the researcher investigates the attack concept in chess, as a
strategic game by exploring virtual chess players of different strategies.
In particular, the researcher explores the performance of an attacking
grandmaster against three other class-A players of different chess person-
alities that vary in controlling the center of the chessboard. To this end,
the researcher collected data from four virtual chess players: a grandmas-
ter and three class-A players. The selected grandmaster is Josh Waitzkin
who is known for his fearless attacking style and deep endgame under-
standing. The class-A players have different personalities: (1) a player
who strongly controls the center of the board; (2) a player who ignores
the center; and (3) a player who offers traps to control his opponent.
The researcher measured different dependent variables including the er-
rors of the players and the moves of the games. The findings show that
class-A players of particular chess personalities perform differently. Ad-
ditionally, the study reveals that there is a positive relationship between
the findings and some of the existing real-life scenarios in the business
Keywords: chess, chess personality, virtual humans, attack, Josh Wait-
zkin, grandmaster
1 Introduction
In the last twenty years, the chess world witnessed many interdisciplinary
endeavors from the fields of psychology, artificial intelligence, and com-
puter design. This resulted in many significant achievements including a
machine being able to defeat Gary Kasparov, the reigning world cham-
pion in 1997. Even more, current chess applications in the market are
very affordable and capable to compete at the level of a grandmaster,
the highest rank a chess player can attain. Such applications offer use-
ful features to chess players among which are providing hints, analyzing
2 Khaldoon Dhou
chess positions, and allowing a player to play against another player or
computer opponent.
Computer opponents can come in a variety of ways such as chess
engines or virtual humans. They are widely used by chess players for
training and they exist in different personalities. Throughout this paper,
the term chess personality is defined as the attitude of a chess player
during his games such as an attack, defense, or a mixture of both. The
personality of a player can shape the direction of a chess game. For
example, some grandmasters start attacking at an early stage of a game,
and this might cause their opponents to act defensively. Other players
like to get their Queen involved during the opening phase.
Investigating the personalities of virtual chess players can play an
essential role in addressing many research problems. First, there is evi-
dence that employing software loaded with virtual players with different
personalities plays a crucial role in surgical training using a virtual re-
ality simulator [5,32]. Second, existing research suggests that there is a
strong association between chess and business in many aspects such as
patience, long-term thinking, and perseverance [19, 21, 23,29, 30]. Third,
rapid developments in building new chess applications have heightened
the need for understanding chess personalities [14]. Virtual chess players
helped in investigating different playing styles by allowing researchers
to explore games between chess players of various personalities who ex-
isted in different eras [12]. Another key thing to remember is that vir-
tual humans made it possible to further examine world champions such
as Garry Kasparov and many other top grandmasters [12,14, 15]. Ad-
ditionally, relevant work is not only limited to virtual humans, but it
also shows existing studies in virtual environments simulating different
types of biological behaviors such as predator-prey ecosystems and ant
colonies [9, 11, 13, 16, 28]. All these studies showed the effectiveness of
simulated behaviors in coding binary information that is widely used in
many research activities such as perception and visualization [10, 17, 18].
In this paper, we explore four virtual players: a grandmaster and
three other class-A players. The selected grandmaster is Josh Waitzkin,
who is known for being a fearless attacker with a deep endgame un-
derstanding. The class-A personalities vary in their skills with regards
to traps and controlling the center. This paper builds on previous re-
search in virtual chess players and provides an important opportunity
to advance the understanding of chess personalities. The findings should
make important contributions to the fields of psychology and computer
science. The central question in this paper asks about the errors that
virtual players of different personalities make while playing against each
other. The main questions addressed in this paper are:
How does Waitzkin behave while playing against class-A players who
vary in their controlling the center and trapping an opponent’s per-
How are class-A players of various chess personalities related to traps
and controlling the center influenced while competing against Wait-
How is the length of the games played against Waitzkin influenced
by the different class-A players employed in the experiment?
An exploration of attack strategies via the utilization of virtual humans 3
The overall structure of this paper takes the form of six sections in-
cluding this section. Section 2 begins with exploring the related studies
and identifying the gaps where further research is needed; Section 3 is
concerned with the study design; Section 4 analyzes the results of the
experiment and presents the findings; Section 5 provides a general dis-
cussion of the research findings based on the existing literature; finally,
section 6 concludes the paper, and offers suggestions for future research.
2 Related work
A large and growing body of the literature has investigated the psychol-
ogy of chess players. Models were developed over the years to understand
a chess player’s mind. The first model was offered by Cleveland [7] and it
incorporates the most vital aspects in modern chess theory and focuses
on chess development. In 1965, de Groot [8] investigated chess players’
thinking and memory and how they solve chess problems. De Groot
exposed chess players to meaningful chess patterns and asked them to
reconstruct them from memory. He found that masters outperformed
novice chess players. This was followed up by the study of Chase and Si-
mon [6] who did a similar experiment with random chess positions. They
found that chess players of various skills failed to reconstruct them. The
previous two experiments show the importance of chess patterns in a
chess player’s skill.
Chess players vary in their ability and are ranked according to a rat-
ing, which is a number that provides an estimation of a chess player’s
skill measured against other players in the chess community. Many orga-
nizations provide chess ratings such as the World Chess Federation and
the United States Chess Federation (USCF). For example, according to
the USCF [34], the highest title is a Senior Master, which is awarded to
a chess player who maintains a rating of 2400 and above. Below that is
a Master, for a player who maintains a rating between 2200 and 2399;
Expert between 2000 to 2199; class A between 1800 to 1999 and so on.
Chess players are not only characterized by their ratings, but they
can also be described by their chess personalities. These personalities are
reflected by the playing style of a player during different game phases.
For example, Marshall (1942-2017) is a risk-taker who offers sacrifices to
gain an advantage during his games [33]. Exploring chess personalities
has its roots in psychoanalysis and numerous studies have attempted
to investigate it from different angles. For example, Jones [24] explored
the personality of grandmaster Paul Murphy and identified many as-
pects in his playing styles such as attack and piece sacrifice. Similarly,
Karpman [25] explored the topic of chess personalities and the condi-
tions at which certain players play the best. He also identified contrasts
in personalities among chess grandmasters and the reasons behind some
game results. In the same vein, Haran [22] identified five chess personal-
ities depending on the opening variation: normal personality, aggressive
personality, defensive personality, semi-open personality, and positional
4 Khaldoon Dhou
The rapid developments in Artificial Intelligence and Computing
Technology made it possible to include new modern features that en-
able further exploration of chess personalities. Among these features are
the virtual chess players, which are defined as computer programs that
simulate real chess players of different levels from beginners to top-rank
grandmasters. Virtual humans opened new research horizons and offered
alternative prospects on how to analyze chess games and explore the psy-
chology of different players. This began with a study that investigated
virtual grandmasters and class-B players that represent actual human
chess players [12]. In the study, the author explored the personalities of
attacking and defending grandmasters and the outcomes while they com-
pete against less skilled players. They found that an attacker grandmas-
ter tends to have fewer errors than a defensive grandmaster. Similarly, the
author found that the class-B players in the study perform better while
competing against an attacker grandmaster. These findings have grounds
in social sciences as research showed that people instinctively seek to per-
ceive the reasons behind challenging events and how they influence their
existence when they face them [36]. This view is also supported by the
study conducted in [15] showing that an attacker grandmaster performs
better as opposed to a defensive grandmaster while competing against
class-A players. What’s more, these are not the only studies that investi-
gated virtual chess players. A recent study explored the chess personality
of Garry Kasparov and how he is influenced by and influencing class-A
players of different personalities [14]. All these studies have emphasized
the importance of virtual humans and how they are used to explore
the personalities of chess players. It is also important to mention that
emulating real chess players is a major area of interest within the game
industry. One example is Virtual Kasparov [2], developed by Titus, which
incorporates simulated chess players of different personalities. Similarly,
Ubisoft developed the Chessmaster and it offers many virtual players
representing real players including top grandmasters such as Kasparov
and Polgar [33].
Overall, all the studies reviewed here and the tremendous industrial
advancements in virtual humans highlight the need to further investigate
this field of research. These studies clearly indicate that there is a strong
relationship between virtual humans, personalities, and existing real-life
scenarios in different fields of research including marketing, management,
and psychology. However, there remain several aspects of virtual chess
players about which relatively little is known.
3 Method
3.1 Participants
Participants in this study were virtual chess players of different chess per-
sonalities. The data for the study was collected from the games between
the virtual players participating in the experiment. The virtual players
that were chosen for the study simulate Waitzkin, and three other class-
A players of different personalities. Below is the description of each of
the virtual players in the experiment, as provided by Ubisoft [33]:
An exploration of attack strategies via the utilization of virtual humans 5
Waitzkin: a well-known chess grandmaster and author who is char-
acterized by being a courageous attacker with a deep comprehension
of the endgame.
Buck: a player who favors openings that include a significant por-
tion of captured pieces. His vulnerability is his almost negligence of
dominating the center of the chessboard. His USCF rating is 2355.
J.T.: he plays specific openings that are outlined to attract his op-
ponent to fall into a prepared trap. His USCF rating is 2330.
Lili: a player with a comprehensive opening knowledge, however,
she favors particular lines of play that can be insignificantly disad-
vantageous. As opposed to Buck, she has excellent control over the
chessboard. Her USCF rating is 2394.
The selection of these players that represent different personalities
is influenced by the categories of moves offered by Chase and Simon
[6, p. 259] such as opening, exchange, defense, and attack. Additionally,
Chase and Simon [6] explored five chess relations between pieces: attack,
defense, proximity, color, and type. It is essential to note that the ratings
of the class-A players utilized in the experiment were almost identical.
3.2 Materials
The simulations were run using the Chessmaster software offered by
Ubisoft [33]. The software is highly praised in the chess community, it
plays at the same level as a top-rank grandmaster, and is used in different
research projects [12, 14, 15].
In the design of the current experiment, the researcher manipulated
two independent variables: the color of the grandmaster’s pieces, and
the class-A player’s personality. Each class-A player played 78 games
against Waitzkin, where half of the games played by each player were
with the white pieces and the other half was with the black pieces. The
class-A player’s personality independent variable has three levels: the
personalities of Buck, J.T., and Lili. The description of their personalities
was provided in the previous subsection.
The researcher used the Chessmaster for analyzing the games and
used the data in the analysis. That is to say, the design involves three
dependent variables:
The agreement percentage of the moves made by the grandmaster
The agreement percentage of the moves made by a class-A player
The number of moves in the game: a move is defined as the White
player’s move followed by the Black player’s move
3.3 Procedure
Waitzkin played 78 games against each class-A player utilized in the
study. To reduce the chance that the player’s color influences the design,
each player plays half of the games in the experiment with white color
and the other half with black.
6 Khaldoon Dhou
4 Results
The researcher conducted a two-way ANOVA to examine the effect of
two independent variables (grandmaster’s color and class-A player’s chess
personality) on each of the dependent variables. All the effects were con-
sidered statistically significant at p < 0.05. There was a significant main
effect of the class-A player personality on the number of moves in the
games, F(2,228) = 5.607, p= 0.004. Pairwise comparisons indicate that
the significant main effect reflects a significant difference (p= 0.003)
between J.T. (M= 68.410) and Lili (M= 57.372).
There was a significant main effect of the color of the grandmaster on
the agreement percentage of the moves made by Waitzkin, F(1,228) =
5.919, p= 0.016. This indicates that the Chessmaster agrees more with
Waitzkin when he plays with black pieces (M= 98.111%) than when
he plays with white pieces (M= 97.385%). Additionally, there was a
significant main effect of the color of the grandmaster on the agreement
percentage of the moves made by the class-A players, F(1,228) = 11.132,
p= 0.001. More specifically, the Chessmaster agrees more with the class-
A players when Waitzkin plays with the black pieces (M= 94.410%)
than white pieces (92.487%). Similarly, there was a significant main ef-
fect of a class-A player’s personality on the agreement percentage of
the moves made by class-A players participating in the experiment,
F(2,228) = 3.529, p= 0.031. Pairwise comparisons indicate that the
significant main effect reflects significant differences (p= 0.039) between
Buck (M= 92.744%) and Lili (M= 94.513%). Fig. 1 shows the Chess-
master’s agreement percentages on the moves made by Waitzkin and the
other class-A players employed in this study.
5 General discussion
This study set out with the aim of assessing the importance of chess per-
sonality in the errors made by virtual chess players of different personal-
ities. The results of this study indicate that a chess player can perform
differently depending on the personality of his opponent. Surprisingly, the
findings showed that Waitzkin performed better with black pieces as op-
posed to when he had white pieces. Likewise, the virtual class-A players
performed better with the black pieces than when they had white pieces.
It is interesting to note that previous research indicated that the per-
formance of less-skilled chess players, measured by the errors they make
is consistent with the performance of their grandmaster opponent. For
example, in a former study exploring two groups of chess players: grand-
masters and class-B players, the researcher found that class-B players
had fewer errors when they played against Anderssen (i.e. an attacker
grandmaster), as opposed to when they played against Leko (i.e. defen-
sive grandmaster). Similarly, the same study showed that Anderssen had
fewer errors than Leko when they both played against the same class-B
players. One possible explanation of why Waitzkin in this experiment
performed better with black pieces is related to chess opening. A chess
An exploration of attack strategies via the utilization of virtual humans 7
Fig. 1. The Chessmaster’s agreement percentages on the moves made by Waitzkin
and the other class-A players utilized in the study. As shown in the figure, Waitzkin
performed the best while competing against Lili as opposed to the other class-A players.
Likewise, Lili did the best among the other class-A players employed in the study.
player sometimes tends to perform better when he encounters an opening
he is familiar with [14].
Openings are not just important in chess, but existing research in the
marketing domain showed the importance of carefulness in handling a
new product. One example is the purchase of Snapple by Quaker, which
owned Gatorade at that time. On the day of declaring that Quaker would
purchase Snapple for 1.7 billion dollars, their stock price decreased by
about 10%, and three years later, after several attempts to merge Snapple
into their environment with Gatorade, Triarc purchased Snapple for 300
million dollars [26]. The example shows that Snapple and Gatorade are
different brands and should not be treated as equals without carefulness.
Likewise, in chess, openings are different, and not being familiar with
a particular opening can result in making more mistakes and probably
losing a game. This is further evidenced by the outcomes of the analysis
showing that the games against J.T. last the longest (Fig. 2), although
8 Khaldoon Dhou
he has almost the same rating as the other class-A players utilized in
this experiment. However, J.T. utilizes opening traps while playing, and
these can probably make his opponent further resist, especially if not
aware of particular opening lines.
Fig. 2. The average number of moves in the study while competing against Waitzkin.
The figure shows that on average, the games involving J.T., a players who considers
traps in the openings were the longest
Another important finding was that, although having almost the
same rating, the class-A players in the study performed differently while
competing against Waitzkin. The experimental results revealed that the
Chessmaster significantly agrees more on the moves made by Lili than
the moves made by Buck. Controlling the center is an essential chess
strategy and for some chess openings, a player sacrifices material to gain
more control of the center of the board [4, 20, 27]. Interestingly, existing
research shows that controlling the center, and other activities encoun-
tered by students while playing a chess game are analogous to some
management principles [4]. In his research, students learn the concept
of controlling the center of the board, which leads to controlling the
game. Additionally, his research shows that such a concept is compara-
ble to winning the domination in the industry and developing into the
performing standard [4].
An exploration of attack strategies via the utilization of virtual humans 9
The findings in this study further support the idea of exploring the
personalities of chess players and how players perform while competing
against other players. That is to say, the findings of this study are con-
sistent with the findings of other studies involving virtual chess players
confirming that players of the same rating perform differently depend-
ing on the personalities of the opponents they are competing with. For
example, in a previous study that investigates the personality of Garry
Kasparov, the findings show that the performance of Kasparov varied
while competing against other less-skilled players [14].
6 Conclusion
The purpose of the current study was to determine how a chess grand-
master who considers attacking strategies performs while playing against
class-A players of different personalities. For this purpose, the researcher
employs virtual players that simulate Waitzkin, as an attacker grand-
master, and other class-A players. Like real players, virtual players have
certain characteristics and they follow different game strategies such as
attack, defense, and controlling the center. The three class-A players
vary in their personalities: a player who tends to capture the opponent’s
pieces, a player who considers offering traps in the opening phase, and a
player with solid center control and comprehensive opening knowledge.
This study has shown that a grandmaster performs differently de-
pending on his opponent’s strategy. Likewise, players of the same rat-
ing perform differently while competing against the same grandmaster.
These findings are consistent with the previous findings exploring grand-
masters and less skilled players [12,14, 15]. For example, class-B players
of almost the same ratings and different chess personalities had different
reactions while competing with two grandmasters of different styles [12].
Likewise, the same study showed that Anderssen, an aggressive grand-
master performed better than Leko while competing against less skilled
players of the same rating category.
The findings in this study suggest that in general, chess personality
is a crucial factor to consider when evaluating the outcomes of chess
games between different players. In other words, although chess players
are evaluated based on their chess ratings, this does not seem to be the
only way to assess their performance. Additionally, since chess is a fun-
damentally strategic game, these findings can be extended to be explored
in other domains such as psychology, business, and computing. The find-
ings reported here shed new light on the psychology of competition and
how the personalities of virtual humans are related to many aspects in
domains exploring many attitudes such as aggressiveness and defense.
That is to say, the researcher believes that the present study lays the
groundwork for future research into exploring how virtual humans can
aid in understanding new elements in business strategies. To develop a
full picture of the personalities of chess players, additional studies will
be needed that explore different aspects of personalities. For example,
further studies, which take personality traits into account, will need to
be undertaken [1, 3, 31, 35].
10 Khaldoon Dhou
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... Similarly, technologies play an active and important role in the educational environment, whether in schools or educational centers or universities [6]. The IoT technology is increasingly involved in numerous vertical applications that address a range of individual and community needs [7], including media [8] and data conversations [9], banking services [2], detection systems [10,11], smart cities [12,13], military applications [14], shopping and movement facilitations [15], local and international education [16], entertainment and medical services [17], and many others [18][19][20][21][22]. ...
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The term IoT technology is associated with many fields, including scientific, commercial, industrial, health, transportation and other fields, which became a necessity of daily life across all segments of society. Artificial intelligence, the Internet of things, data science and big data are among the most prominent fields of technologies in the modern era of e-knowledge, which are increasingly being relied on in many areas of our life. This research is considered very special and urgent affecting around 20% of the humanities, i.e., Muslim people, whom try to be selected to perform Islamic rituals every year. Given this importance of applying and adapting technologies to serve Hajj and Umrah performers, it is necessary to conduct an analytical study for the technologies and their applications that have been applied in the Hajj and Umrah system. The surveyed studies have been classified, according to their targeted services (focus area), into four main branches, including studies of the two holy mosques and the holy sites, studies of the pre-arrival visitors, housing and services studies, and transportation and crowd management studies. The classified studies were analyzed according to research field, research instrument, statistical method, nature of statistical data, data collection tools, and research citations. The paper outcome opens novel research practical directions selected based on holistic overview of the current technologies analytical survey, which is essential to be considered to improve Hajj and Umrah services coping with today’s technology, in addition to discussing other open issues that need further deep research.
Chess has emerged as a promising domain for psychologists, marketers, computer scientists, and experts from many other domains. In this paper, the author explores attack behavior in chess via the employment of virtual chess players. In other words, he investigates two virtual grandmasters: Chigorin and Waitzkin. Although the two grandmasters have the same level of strength, they have two different chess personalities that determine their attitudes during chess games. While Waitzkin is an attacker grandmaster, Chigorin tends to be a feared attacker, and he leans more toward the defensive side. The investigation is conducted by collecting data from the two grandmasters while competing against three other class-A virtual players. The results were consistent with the previous research that shows the importance of chess strategy in the outcomes of the game. Generally, a chess grandmaster with an attacking strategy performed better than a grandmaster with a defensive strategy. Likewise, the less skilled players (i.e., class-A virtual players) performed differently although their ratings are almost the same. These findings can be valuable to chess software designers and for training chess players of different skills and personalities.Keywordschess personalityattackdefensevirtual humangrandmaster
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.
The accelerated developments in technology led to a tremendous increase in the volumes of data to be transferred and exchanged between various network channels. These advancements create a huge demand for researchers to investigate new data compression techniques. Recent evidence from the literature shows that agent-based modeling is a promising direction to reduce the size of the data and change its original representation. In this article, the objective is to build an agent-based modeling simulation for chain coding and take advantage of it in data compression. Our agent-based model is inspired by the concept of defended territories of biological beavers. To this end, we use the pixel distribution in a bi-level image to construct a virtual environment of agents, add the beavers, and build territories around them. The main idea of defended beaver territories is to allow each beaver to maintain its area and protects it from intruders. To put it another way, defended territories allow beavers to work on different parts of an image while the algorithm tracks and records their movements, as well as manages disputes between them. Our research findings represent a further step towards employing the generated codes of movements in image processing operations other than coding and compression. Additionally, the experimental results showed that the current model was prosperous, and it could outperform many existing image compression techniques, including JBIG family methods. What’s more, paired-samples t-tests reveal that the mean differences between the outcomes of the current approach and each of the other standardized benchmarks we employed in comparison are statistically significant.
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The present study analyzed the relationships between directive, self and social functions of autobiographical memory, personality traits, as defined by the Five Factor model, and the Facebook mementos. We defined Facebook mementos as objective measures of the textual (i.e., Facebook Status Updating) and visual (i.e., Photos uploading) information people record on their Facebook profiles. Questionnaires gathered data from a sample of 193 Italian Facebook users (148 female; 45 male; age M = 22.8, SD = 6.8). Results at path analysis using AMOS showed direct significant positive associations between personality traits related to extraversion, openness, neuroticism, conscientiousness and Facebook mementos. Extraversion and openness were positive precursors of the directive, self and social functions of autobiographical memory, whereas neuroticism predicted directive and self-functions, and conscientiousness was a positive precursor of the directive function of autobiographical memory. As well, indirect significant positive paths among extraversion, neuroticism, openness and the frequency of photos uploaded on Facebook for collecting life events have emerged via the mediation of the self-continuity function of autobiographical memory. In sum, the present study highlights how individual differences in motivations for using autobiographical memory for directive-behavior, self-continuity or social-bonding purposes deeply related with the personal experience of using social media as a repository tool for textual or visual information.
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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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In this article, the researcher develops an image coding technique which is based on the wolf-sheep predation model. In the design, images are converted to virtual worlds of sheep, routes and wolves. Wolves in this model wander around searching for sheep while the algorithm tracks their movement. A wolf has seven movements which capture all the directions of the wolf. In addition, the researcher introduces one extra move of the wolf the purpose of which is to provide a shorter string of movements and to enhance the compression ratio. The first coordinates and the movements of the wolf are tracked and recorded. Then, arithmetic coding is applied on the string of movements to further compress it. The algorithm was applied on a set of images and the results were compared with other algorithms in the research community. The experimental results reveal that the size of the compressed string of wolf movements offer a higher reduction in space and the compression ratio is higher than those of many existing compression algorithms including G3, G4, JBIG1, JBIG2 and the recent agent-based model of ant colonies.
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Virtual humans emerged as a topic of research in HCI and they have been used for various purposes. This paper explores the behavior of chess players in a virtual chess environment to gain more understanding about chess personalities. In particular, the focus of this research is investigating attack and defense strategies used by virtual chess grandmasters against different virtual class-B personalities who vary in their strength in the different stages of a game. These attack and defense strategies have attracted much attention in the chess community and are considered among the main aspects to chess players. They occur in different phases of the game: opening, middle game and endgame. The researcher examines virtual chess players to understand the psychology of competition between two grandmasters (attacker, defender) and three class-B chess players with different personalities: (a) strong at openings; (b) weak at openings, but strong at endgames and (c) balanced player. The virtual humans in this research represent personalities of real players. The empirical players' results showed that the personalities could influence the error and the number of moves of the game for both grandmasters and class-B players. Such findings can be used in designing virtual chess players.
The topic of virtual humans is increasingly vital in entertainment. They offer an influential medium for amusement and learning. In this article, the researcher investigates virtual chess players of different personalities to explore the psychology of competition between two groups of virtual chess players: grandmasters and class-A players. More specifically, the researcher evaluates the different errors made by the two groups of virtual players while competing against each other. The two virtual grandmasters are represented by Anderssen and Leko, who vary in their attack and defense styles. While Anderssen is an aggressive grandmaster, who starts attacking his opponent at an early stage of the game, Leko is known for being a solid defensive player. The class-A players in this study vary in their Knight-Bishop employment preferences. The study reveals many interesting findings of the errors made by different virtual chess players. These findings have their grounds in social sciences and can be beneficial to psychology and computing researchers.
The economic literature on time-pressured decisions is slim and has mainly relied on laboratory collected data. Within this literature, only few studies have investigated the gender differences in the effects of time constraint on decision making. The World Chess Federation reports the official player ratings in Standard, Rapid and Blitz Chess. Standard, Rapid, and Blitz Chess only differ from each other in the stringency of the time constraint they impose on the players. While Standard Chess can last several hours, Rapid (Blitz) Chess allocates each player (30) 10 min or less for the entire game. The present paper uses 2012 to 2019 chess tournament data of the World Chess Federation to investigate the gender differences in the effects of time constraint on performance. These data, containing more than 1.8 million individual observations, are analyzed using several approaches to ensure the robustness of the findings. The results indicate that the Rapid and Blitz ratings of female chess players are below the ratings of male chess players of the same skills. While female underperformance is largely robust even at the country level, its magnitude is rather small. Finally, reexamining the question by chess skill thresholds produces evidence that female underperformance is greater among the elite players. The findings are discussed in light of the past literature.
In this paper, the researcher introduces a new chain coding mechanism that employs a predator–prey agent-based modeling simulation with some modifications and simplifications and uses it in compression. In the proposed method, an image is ultimately represented by a virtual world consisting of three agent types: wolves, sheep, and paths. While sheep and paths do not change their locations during the program execution, wolves search for sheep to prey on. With each step, a wolf can determine its path by choosing between seven pertinent moves depending on the encountered information. The algorithm keeps track of the wolves’ initial locations and their movements and uses this as a new image representation. Additionally, the researcher introduces the ‘Lengthy Advance Move,’ the purpose of which is to group particular consecutive codes and further reduce the chain. This, in turn, allows the researcher to experiment with different variations of the algorithm. Finally, the researcher applies arithmetic coding on the series of movements for extra compression. The experimental results reveal that the current algorithm generates higher compression ratios than many standardized algorithms, including JBIG family algorithms. Most importantly, paired-sample t-tests reveal significant differences between the findings of the wolf-sheep predation algorithm and the other algorithms used as benchmarks for comparisons.
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.