Machine Learning Techniques for Analyzing Training Behavior in Serious Gaming

Gombolay, Matthew; Jensen, Reed; Son, Sung-Hyun

doi:10.1109/tciaig.2017.2754375

Cited by 12 publications

(7 citation statements)

References 33 publications

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“…Another option would be to train operators to never simply accept a recommendation without first exploring the solution space and also to specifically remember to search down in an image. Previous research has shown that HMMs could be used to develop automated training aids (Gombolay, Jensen, & Son, 2017), so we leave these areas for future work but believe that these results demonstrate the utility of adding such an HMM analysis to any complex of human–computer study in a supervisory control setting.…”

Section: Resultsmentioning

confidence: 86%

The Impact of Increasing Autonomy on Training Requirements in a UAV Supervisory Control Task

Cummings

Huang

Zhu

et al. 2019

Journal of Cognitive Engineering and Decision Making

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A common assumption across many industries is that inserting advanced autonomy can often replace humans for low-level tasks, with cost reduction benefits. However, humans are often only partially replaced and moved into a supervisory capacity with reduced training. It is not clear how this shift from human to automation control and subsequent training reduction influences human performance, errors, and a tendency toward automation bias. To this end, a study was conducted to determine whether adding autonomy and skipping skill-based training could influence performance in a supervisory control task. In the human-in-the-loop experiment, operators performed unmanned aerial vehicle (UAV) search tasks with varying degrees of autonomy and training. At the lowest level of autonomy, operators searched images and, at the highest level, an automated target recognition algorithm presented its best estimate of a possible target, occasionally incorrectly. Results were mixed, with search time not affected by skill-based training. However, novices with skill-based training and automated target search misclassified more targets, suggesting a propensity toward automation bias. More experienced operators had significantly fewer misclassifications when the autonomy erred. A descriptive machine learning model in the form of a hidden Markov model also provided new insights for improved training protocols and interventional technologies.

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Section: Resultsmentioning

confidence: 86%

The Impact of Increasing Autonomy on Training Requirements in a UAV Supervisory Control Task

Cummings

Huang

Zhu

et al. 2019

Journal of Cognitive Engineering and Decision Making

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“…Similarly, we can let another experienced developer to process the jobs of modeling all the different civilians. For more research into game development, please refer to [28][29][30][31][32][33][34][35][36].…”

Section: Related Workmentioning

confidence: 99%

Minimizing the Total Tardiness of a Game Project Considering the Overlap Effect

2020

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There has long been a custom that game development is not the mainstream of engineering and its tardiness brings little or even no harm to this industry. Nowadays, the pendulum of industrial development has swung to another side. A game project may involve hundreds of developers, thousands of jobs, and millions of costs. Situations would be worse if jobs overlap with others. Any delay of a large game project can cause a heavy penalty. Consequently, in this study, we propose two scheduling algorithms to reduce the total tardiness of a game project. If the problem size is small, a branch-and-bound algorithm is employed to provide the optimal schedules; otherwise a genetic algorithm is used to generate near-optimal schedules. The experimental results show that the proposed algorithms reduce the total tardiness significantly.

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“…ML can provide a trustworthy and efficient method for addressing complicated challenges in real-world applications. For instance medicine [4,5], Information security [6], gaming [7,8], sports [9], and energy consumption [10] and many others.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning with Supervised Machine Learning Models to Predict Credit Card Fraud Transactions

Baker¹,

Mahmood²,

Shaker³

2022

RIA

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In recent years, the highly boosting development in e-commerce technologies made it possible for people to select the most desirable items from shops and stores worldwide while being at home. Credit card frauds transactions are common nowadays because of online payments. Online transactions are the root cause of fraudulent credit card activity, bringing enormous financial losses. Financial institutions must install an automatic deterrent mechanism to check these fraudulent actions. The fraudulent transactions do not follow a specific pattern and continuously change their shape and behavior. This paper aims to use ensemble learning with supervised Machine Learning (ML) models to predict the occurrence of fraud transactions. The experimental study has been evaluated on the open-source Kaggle credit card fraud detection dataset. The performance of the proposed model is measured in terms of accuracy score, confusion matrix, and classification report. The results were state-of-the-art using the voting ensemble learning technique shows that it can be get the best results using PCA with 100.0% accuracy, 97.3% precision, 73.5% recall, and 83.7% f1-score against other ML classifiers.

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Machine Learning Techniques for Analyzing Training Behavior in Serious Gaming

Cited by 12 publications

References 33 publications

The Impact of Increasing Autonomy on Training Requirements in a UAV Supervisory Control Task

The Impact of Increasing Autonomy on Training Requirements in a UAV Supervisory Control Task

Minimizing the Total Tardiness of a Game Project Considering the Overlap Effect

Ensemble Learning with Supervised Machine Learning Models to Predict Credit Card Fraud Transactions

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