Asyncflow: A visual programming tool for game artificial intelligence

Hu, Zhaoyang; Fan, Changjie; Zheng, Qiwei; Wu, Wei; Bai, Lu

doi:10.1016/j.visinf.2021.11.001

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…It makes them feel less lonely to learn, dare to ask questions, and enjoy learning. The findings of this study align with the outcomes of other research investigations [6,19,20]. It suggests that the use of visual tools for the analysis and design of program processes can enhance students' motivation to study and foster the development of logical thinking abilities.…”

Section: Discussionsupporting

confidence: 85%

“…It enables students to practice systematic reasoning, deduction, and problem-solving through logical steps. Consequently, flowcharts lead to a deeper understanding of the relationship between programming elements, resulting in improved code design and implementation [6,20]. Educators have recognized the value of flowcharts in computer programming education and integrated this technique into various teaching methodologies, such as problem-based learning and collaborative learning.…”

Section: B Thinking Process Model Technique In Computer Programmingmentioning

confidence: 99%

“…The thinking process model technique as a flowchart is widely used in computer programming courses because it facilitates programming logic comprehension, encourages algorithm development, and promotes logical thinking. Therefore, incorporating flowcharts into various teaching methodologies underscores their significance in equipping students for successful programming careers [5,6]. In addition, online collaborative learning fosters effective collaboration skills among students by allowing them to participate actively in their learning process without limiting learning location, resulting in high student performance as they share their knowledge and learn from one another.…”

Section: Introductionmentioning

confidence: 99%

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Integrating Thinking Process Model Technique with Online Collaborative Learning to Promote Programming Logical Thinking

Daungcharone

2024

IJIET

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Logical thinking is essential for students learning computer programming, enabling them to analyze problems and design solutions effectively. Flowcharts, a thinking process model technique, are particularly useful for illustrating program processes and fostering logical reasoning. Additionally, transitioning to a collaborative learning environment enhances students’ social skills and motivation through group interactions. This study explored the impact of a novel learning approach that merges the thinking process model with online collaborative learning among 410 university students. It examined how different learning approaches and individual preferences affected their learning achievement and motivation. The findings indicate that learning achievement varied significantly with different learning approaches and individual preferences but showed no significant variance when combining both factors. For learning motivation, significant differences were noted across all dimensions with varying learning approaches, and some dimensions were affected by individual preferences, with no significant change observed across all dimensions when combining learning approaches and individual preferences. The research concludes that incorporating thinking process models and collaborative learning can substantially improve students’ learning outcomes and motivation.

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Section: Discussionsupporting

confidence: 85%

Section: B Thinking Process Model Technique In Computer Programmingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating Thinking Process Model Technique with Online Collaborative Learning to Promote Programming Logical Thinking

Daungcharone

2024

IJIET

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“…In 1956, John McCarthy coined the term “artificial intelligence” (AI), marking the inception of a computer science subfield centered on machine learning (ML) and an aspiration to emulate human intelligence . AI-driven algorithms have since permeated diverse disciplines, , with notable advancements in sectors such as image understanding, pattern recognition, autonomous driving, automatic programming, big data, , robotics, and human-machine collaboration . Despite their transformative potential, the inner mechanics of AI remain obscured.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the UV-Fenton Degradation of m-Cresol Wastewater: An Experimental and Artificial Intelligence Modeling Approach

Zhang,

Yao,

Zhao

et al. 2024

Ind. Eng. Chem. Res.

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Wastewater treatment, especially the efficient degradation of contaminants such as m-cresol, remains a pivotal challenge. This study investigates the application of artificial neural networks (ANN) in predicting total organic carbon (TOC) removal rates from m-cresol-contaminated wastewater by using the ultraviolet (UV)-Fenton oxidation process. Six key variables, namely, Fe 2+ dosage, H 2 O 2 dosage, catalyst quantity, reaction time, pH, and substrate concentration, were employed as inputs to the ANN model. Leveraging this multivariable input and a comprehensive data set, the ANN model projected a maximum TOC removal rate of 87.12%, validated by an efficiency of 86.26% achieved through experiments under the derived optimal conditions: Fe 2+ dosage at 16.09 mg/L, H 2 O 2 dosage at 1.40 mg/L, catalyst quantity at 0.11 g/L, reaction time of 29.80 min, initial pH of 3.66, and substrate concentration of 50 mg/L. Comparative analysis with other machine learning algorithms further revealed that the ANN model notably outperformed linear regression, support vector regression, and random forest in terms of precision. This work paves the way for resource-optimized experimental designs, fostering real-time wastewater monitoring and refining advanced oxidation process proficiency in industrial applications.

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“…Moreover, the language module and decoding layer of the Pix2code are strengthened adopting BLSTM to improve the model's word prediction ability and robustness. Asyncflow, which includes a flowchart generator interpreted by game logic and an asynchronous mechanism runtime framework based on an event driven architecture [13]. In response to the issue of building user interface systems, Ren S et al studied that in this environment, users can use an aWeb browser to process domain specific languages (such as FAUST or Gen) with audio for graphical design and operation of digital signal processor algorithms.…”

Section: Introductionmentioning

confidence: 99%

Automatic Code Generation for Android Applications Based on Improved Pix2code

Zou,

2024

JAIT

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With the expansion of the Internet market, the traditional software development method has been difficult to meet the market demand due to the problems of long development cycle, tedious work and difficult system maintenance. Therefore, to improve software development efficiency, this study uses residual networks and bidirectional long short-term memory networks to improve the Pix2code model. The experiment results show that after improving the visual module of the Pix2code model using residual networks, the accuracy of the training set improves from 0.92 to 0.96, and the convergence time is shortened from 3 hours to 2 hours. After using a bidirectional long short-term memory network to improve the language module and decoding layer, the accuracy and convergence speed of the model have also been improved. The accuracy of the training set grew from 0.88 to 0.92, and the convergence time was shortened by 0.5 hours. However, models improved by bidirectional long short-term memory networks might exhibit over-fitting, and thus this study uses Dropout and Xavier normal distribution to improve the memory network. The results validate that the training set accuracy of the optimized bidirectional long short-term memory network remains around 0.92, but the accuracy of the test set has improved to a maximum of 85%. Dropout and Xavier normal distributions can effectively improve the over-fitting problem of bidirectional long short-term memory networks. Although they can also decrease the model’s stability, their gain is higher. The training and testing accuracy of the Pix2code improved by residual network and bidirectional long short-term memory network are 0.95 and 0.82, respectively, while the code generation accuracy of the original Pix2code is only 0.77. The above data indicates that the improved Pix2code model has improved the accuracy and stability of code automatic generation.

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Asyncflow: A visual programming tool for game artificial intelligence

Cited by 6 publications

References 11 publications

Integrating Thinking Process Model Technique with Online Collaborative Learning to Promote Programming Logical Thinking

Integrating Thinking Process Model Technique with Online Collaborative Learning to Promote Programming Logical Thinking

Optimizing the UV-Fenton Degradation of m-Cresol Wastewater: An Experimental and Artificial Intelligence Modeling Approach

Automatic Code Generation for Android Applications Based on Improved Pix2code

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