Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
We show that the discrete real option game model proposed in the recent literature can be extended to the case of imperfect information. As a result, the model can cover a wider range of applications. However, we also observe that the effectiveness of implementing the subsidy is affected by the imperfect informational structure....
Digital game addiction and problematic internet use have emerged as significant issues, attracting growing attention from educators, psychologists, and policymakers. This study aimed to examine the mediating role of emotional regulation self-efficacy and the moderating role of problematic internet use in the effect of digital game addiction on academic motivation in Turkish adolescents. A correlational research method was utilized to address research questions. A total of 1156 high school students voluntarily participated in the study. Self-report questionnaires (the Short Academic Motivation Scale, Digital Game Addiction Scale, Regulatory Emotional Self-Efficacy Scale and Young’s Internet Addiction Scale Short Form) were used to collect data in 2024. In the analysis of the data, Pearson Product Moment Correlation Coefficient, mediator and moderator analyses were conducted using statistical software. The analysis provided evidence of the negative effect of digital game addiction on academic motivation. Additionally, emotional regulation self-efficacy was found to partly mediate the relationship between digital game addiction and academic motivation. Furthermore, problematic internet use moderated the relationship between digital game addiction and academic motivation in adolescents. The results suggested enhancing adolescents’ emotional regulation self-efficacy and reducing problematic internet use are crucial steps towards mitigating the negative effects of digital game addiction on academic motivation....
This study presents an AI-based sports broadcasting system capable of real-time game analysis and automated commentary. The model first acquires essential background knowledge, including the court layout, game rules, team information, and player details. YOLO model-based segmentation is applied for a local camera view to enhance court recognition accuracy. Player’s actions and ball tracking is performed through YOLO algorithms. In each frame, the YOLO detection model is used to detect the bounding boxes of the players. Then, we proposed our tracking algorithm, which computed the IoU from previous frames and linked together to track the movement paths of the players. Player behavior is achieved via the R(2+1)D action recognition model including player actions such as running, dribbling, shooting, and blocking. The system demonstrates high performance, achieving an average accuracy of 97% in court calibration, 92.5% in player and object detection, and 85.04% in action recognition. Key game events are identified based on positional and action data, with broadcast lines generated using GPT APIs and converted to natural audio commentary via Text-to-Speech (TTS). This system offers a comprehensive framework for automating sports broadcasting with advanced AI techniques....
Games provide an excellent testing ground for machine learning and artificial intelligence, offering diverse environments with strategic challenges and complex decisionmaking scenarios. This study seeks to design a self-learning artificial intelligent agent capable of playing the trick-taking stage of the popular card game Thousand, known for its complex bidding system and dynamic gameplay. Due to the game’s vast state space and strategic complexity, other artificial intelligence approaches, such as Monte Carlo Tree Search and Deep Counterfactual Regret Minimisation, are infeasible. To address these challenges, the enhanced version of the REINFORCE policy gradient algorithm is proposed. Introducing a score-related parameter β designed to guide the learning process by prioritising valuable games, the proposed approach enhances policy updates and improves overall learning outcomes. Moreover, leveraging the off-policy experience replay, along with the importance weighting of behavioural policy, enhanced training stability and reduced model variance. The proposed algorithm was applied to the trick-taking stage of the popular game Thousand Schnapsen in a two-player setup. Four distinct neural network models were explored to evaluate the performance of the proposed approach. A custom test suite of selected deals and tournament evaluations was employed to assess effectiveness. Comparisons were made against two benchmark strategies: a random strategy agent and an alpha-beta pruning tree search with varying search depths. The proposed algorithm achieved win rates exceeding 65% against the random agent, nearly 60% against alpha-beta pruning at a search depth of 6, and 55% against alpha-beta pruning at the maximum possible depth....
The computational complexity of large-scale networked evolutionary games has become a challenging problem. Based on network aggregation and pinning control methods, this paper investigates the problem of control design for strategy consensus of large-scale networked evolutionary games. The large-size network is divided into several small subnetworks by the aggregation method, and a pinning control algorithm is proposed to achieve the strategy consensus of small subnetworks. Then, the matchable condition between the small subnetworks is realized by the input–output control. Finally, some sufficient conditions as well as an algorithm are proposed for the strategy consensus of large-scale networked evolutionary games....
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