Artificial intelligence unmasks anonymous chess players

PurposeThis study aims to investigate the cybersecurity awareness manifested as protective behavior to explain self-disclosure in social networking sites. The disclosure of information about oneself is associated with benefits as well as privacy risks. The individuals self-disclose to gain social capital and display protective behaviors to evade privacy risks by careful cost-benefit calculation of disclosing information.Design/methodology/approachThis study explores the role of cyber protection behavior in predicting self-disclosure along with demographics (age and gender) and digital divide (frequency of Internet access) variables by conducting a face-to-face survey. Data were collected from 284 participants. The model is validated by using multiple hierarchal regression along with the artificial intelligence approach.FindingsThe results revealed that cyber protection behavior significantly explains the variance in self-disclosure behavior. The complementary use of five machine learning (ML) algorithms further validated the model. The ML algorithms predicted self-disclosure with an area under the curve of 0.74 and an F1 measure of 0.70.Practical implicationsThe findings suggest that costs associated with self-disclosure can be mitigated by educating the individuals to heighten their cybersecurity awareness through cybersecurity training programs.Originality/valueThis study uses a hybrid approach to assess the influence of cyber protection behavior on self-disclosure using expectant valence theory (EVT).

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AI unmasks anonymous chess players, posing privacy risks

AAAS Articles DO Group ◽

10.1126/science.ada0080 ◽

2022 ◽

Keyword(s):

Privacy Risks ◽

Chess Players

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Analysis of Minimax Algorithm Using Tic-Tac-Toe

Intelligent Systems and Computer Technology - Advances in Parallel Computing ◽

10.3233/apc200197 ◽

2020 ◽

Author(s):

Swaminathan B ◽

Vaishali R ◽

subashri T S R

Keyword(s):

Artificial Intelligence ◽

Development Time ◽

Effective Rate ◽

Optimal Solutions ◽

Repetitive Work ◽

Computer Chess ◽

Game Industry ◽

Game Developers ◽

Chess Players ◽

Intensive Work

The game industry has been on exponential growth, has different businesses of varying size, ethos, scope and beyond. Success of these video-games comes from a lot of labor-intensive work by developers. Every little nuance of each character, the objects within a character’s environment must be hand-coded. Repetitive work takes up a significant part of development time, which leads to an increase in glitches and logical flaws. Artificial intelligence has been used to simulate human players in software games, provides an opportunity for game developers to create unique experiences and different outcomes for each player. Computer chess players are well-known examples, wherein modern chess programs are trained to defeat best human players. AI based algorithms that can be implemented for games, but a need for optimal solutions is on a rise. We require a comparative analysis of multiple algorithms for understanding the most efficient and ideal one. In our work, through use of a game Tic-Tac-Toe various algorithms will be carried out with its prototype compared in terms of effective rate and optimality.

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