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

Dhou, Khaldoon

doi:10.1007/978-3-030-30033-3_24

Cited by 13 publications

(3 citation statements)

References 40 publications

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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].…”

Section: Literature Reviewmentioning

confidence: 99%

Influence of personality traits on users’ viewing behaviour

Sarsam

Al-Samarraie

Alzahrani

2021

Journal of Information Science

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Different views on the role of personal factors in moderating individual viewing behaviour exist. This study examined the impact of personality traits on individual viewing behaviour of facial stimulus. A total of 96 students (46 males and 50 females, age 23–28 years) were participated in this study. The Big-Five personality traits of all the participants together with data related to their eye-movements were collected and analysed. The results showed three groups of users who scored high on the personality traits of neuroticism, agreeableness and conscientiousness. Individuals who scored high in a specific personality trait were more probably to interpret the visual image differently from individuals with other personality traits. To determine the extent to which a specific personality trait is associated with users’ viewing behaviour of visual stimulus, a predictive model was developed and validated. The prediction results showed that 96.73% of the identified personality traits can potentially be predicted by the viewing behaviour of users. The findings of this study can expand the current understanding of human personality and choice behaviour. The study also contributes to the perceptual encoding process of faces and the perceptual mechanism in the holistic face processing theory.

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Section: Literature Reviewmentioning

confidence: 99%

Influence of personality traits on users’ viewing behaviour

Sarsam

Al-Samarraie

Alzahrani

2021

Journal of Information Science

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“…Over the past few decades, the five-factor (Big Five) model has emerged as one of the dominant models of personality that can be used to understand humans’ behavior (Dhou, 2018, 2019) The five-factor model has been widely used to explore various personal psychological aspects (Al-Samarraie et al, 2016, 2018). This makes personality an important factor for sarcasm detection-related tasks.…”

Section: Common Features Of Sarcasm Detectionmentioning

confidence: 99%

Sarcasm detection using machine learning algorithms in Twitter: A systematic review

Sarsam

Al-Samarraie

Alzahrani

et al. 2020

International Journal of Market Research

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Recognizing both literal and figurative meanings is crucial to understanding users’ opinions on various topics or events in social media. Detecting the sarcastic posts on social media has received much attention recently, particularly because sarcastic comments in the form of tweets often include positive words that represent negative or undesirable characteristics. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used to understand the application of different machine learning algorithms for sarcasm detection in Twitter. Extensive database searching led to the inclusion of 31 studies classified into two groups: Adapted Machine Learning Algorithms (AMLA) and Customized Machine Learning Algorithms (CMLA). The review results revealed that Support Vector Machine (SVM) was the best and the most commonly used AMLA for sarcasm detection in Twitter. In addition, combining Convolutional Neural Network (CNN) and SVM was found to offer a high prediction accuracy. Moreover, our result showed that using lexical, pragmatic, frequency, and part-of-speech tagging can contribute to the performance of SVM, whereas both lexical and personal features can enhance the performance of CNN-SVM. This work also addressed the main challenges faced by prior scholars when predicting sarcastic tweets. Such knowledge can be useful for future researchers or machine learning developers to consider the major issues of classifying sarcastic posts in social media.

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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.…”

Section: Some Examplesmentioning

confidence: 99%

An Algorithm for Automatically Updating a Forsyth-Edwards Notation String Without an Array Board Representation

Iqbal

2020

2020 8th International Conference on Information Technology and Multimedia (ICIMU)

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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.

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

Cited by 13 publications

References 40 publications

Influence of personality traits on users’ viewing behaviour

Influence of personality traits on users’ viewing behaviour

Sarcasm detection using machine learning algorithms in Twitter: A systematic review

An Algorithm for Automatically Updating a Forsyth-Edwards Notation String Without an Array Board Representation

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