Current Issue : April - June Volume : 2018 Issue Number : 2 Articles : 5 Articles
In this research, we aim to create a computer player that gives fun to the opponent.\nResearch on game AI has spread widely in recent years, and many\ngames are being studied. Some of those studies have made remarkable results.\nGame research is aimed at strengthening computer players. However, it is\nunknown whether a computer player who is too strong is good. There may\nalso be opponents who think that a computer player is not interesting if it is\ntoo strong. Therefore, we thought whether we could create a computer player\nwho entertains the opponent while maintaining a certain degree of strength.\nTo realize this idea, we use the Monte Carlo Tree Search. We tried to create a\ncomputer player that gives fun to the opponent by improving the Monte Carlo\nTree Search. As a result of some experiments, we succeeded in giving fun, although\nit was a first step. On the other hand, many problems were found\nthrough experiments. In future, it is necessary to solve these problems....
The success and wider adaptability of smart phones has given a new dimension to\nthe gaming industry. Due to the wide spectrum of video games, the success of a particular\ngame depends on how efficiently it is able to capture the end users� attention. This leads to\nthe need to analyse the cognitive aspects of the end user, that is the game player, during game\nplay. A direct window to see how an end user responds to a stimuli is to look at their brain\nactivity. In this study, electroencephalography (EEG) is used to record human brain activity during\ngame play. A commercially available EEG headset is used for this purpose giving fourteen channels\nof recorded EEG brain activity. The aim is to classify a player as expert or novice using the brain\nactivity as the player indulges in the game play. Three different machine learning classifiers have been\nused to train and test the system. Among the classifiers, naive Bayes has outperformed others with\nan accuracy of 88%, when data from all fourteen EEG channels are used. Furthermore, the activity\nobserved on electrodes is statistically analysed and mapped for brain visualizations. The analysis has\nshown that out of the available fourteen channels, only four channels in the frontal and occipital brain\nregions show significant activity. Features of these four channels are then used, and the performance\nparameters of the four-channel classification are compared to the results of the fourteen-channel\nclassification. It has been observed that support vector machine and the naive Bayes give good\nclassification accuracy and processing time, well suited for real-time applications....
This paper proposes an adaptive Kalman filter (AKF) to improve the performance\nof a vision-based human machine interface (HMI) applied to a video game. The HMI identifies\nhead gestures and decodes them into corresponding commands. Face detection and feature tracking\nalgorithms are used to detect optical flow produced by head gestures. Such approaches often fail due\nto changes in head posture, occlusion and varying illumination. The adaptive Kalman filter is applied\nto estimate motion information and reduce the effect of missing frames in a real-time application.\nFailure in head gesture tracking eventually leads to malfunctioning game control, reducing the scores\nachieved, so the performance of the proposed vision-based HMI is examined using a game scoring\nmechanism. The experimental results show that the proposed interface has a good response time,\nand the adaptive Kalman filter improves the game scores by ten percent....
Gaming behaviors have been significantly influenced by smartphones. This study was\ndesigned to explore gaming behaviors and clinical characteristics across different gaming device\nusage patterns and the role of the patterns on Internet gaming disorder (IGD). Responders of an online\nsurvey regarding smartphone and online game usage were classified by different gaming device usage\npatterns: (1) individuals who played only computer games; (2) individuals who played computer\ngames more than smartphone games; (3) individuals who played computer and smartphone games\nevenly; (4) individuals who played smartphone games more than computer games; (5) individuals\nwho played only smartphone games. Data on demographics, gaming-related behaviors, and scales for\nInternet and smartphone addiction, depression, anxiety disorder, and substance use were collected.\nCombined users, especially those who played computer and smartphone games evenly, had higher\nprevalence of IGD, depression, anxiety disorder, and substance use disorder. These subjects were more\nprone to develop IGD than reference group (computer only gamers) (B = 0.457, odds ratio = 1.579).\nSmartphone only gamers had the lowest prevalence of IGD, spent the least time and money on\ngaming, and showed lowest scores of Internet and smartphone addiction. Our findings suggest that\ngaming device usage patterns may be associated with the occurrence, course, and prognosis of IGD....
This study examines the relationship between player�s value systems and their actions in playing a massively multiplayer online\nrole-playing game. Online survey data from 1,577 players were paired with their behavioral metrics within the game. A number\nof correlations were found between the scores of value system and the in-game metrics. Participants that scored high on the Red\nvalue system tend to spend more real money in the game, level up their character and ability as quickly as possible, and seek\nother achievements in the forms offered by game world. These characteristics for fun, power, and immediate gratification are also\npredicted by the Red value system. The finding provides valuable information on how to better design, evaluate, and understand\nenjoyment in games. The results also show the possibility of using the game as a platform in inferring players� value systems and in\ntraining people to develop certain skills...
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