Unterschiede
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Beide Seiten der vorigen Revision Vorhergehende Überarbeitung Nächste Überarbeitung | Vorhergehende Überarbeitung | ||
arbeiten:latencycsgo [27.04.2021 20:12] – David Halbhuber | arbeiten:latencycsgo [24.01.2022 11:10] (aktuell) – [Data-Entry] David Halbhuber | ||
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- | ====== Development and Evaluation of a Ingame Latency Compensation Technique Artificial Neural Networks ====== | + | ====== Development and Evaluation of Ingame Latency Compensation Technique |
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- | Thema : Development and Evaluation of a Ingame Latency Compensation Technique Artificial Neural Networks | + | Thema : Development and Evaluation of Ingame Latency Compensation Technique |
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- | ZweitgutachterIn_secondthesisprofessor : # | + | ZweitgutachterIn_secondthesisprofessor : Valentin Schwind |
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=== Hintergrund === | === Hintergrund === | ||
- | “Latency from a general point of view is a time delay between the cause and the effect of some physical change in the system being observed” [1]. Latency in video games impairs the performance of the player and game experience. The performance in games, which require precision and have tight deadlines | + | “Latency from a general point of view is a time delay between the cause and the effect of some physical change in the system being observed” [1]. Latency in video games impairs the performance of the player and game experience. The performance in fast-paced |
- | • Delayed input techniques try to add delay to local actions to allow simultaneous execution by all clients [4, 5]. | + | * Delayed input techniques try to add delay to local actions to allow simultaneous execution by all clients [4, 5]. |
- | • Time-offsetting techniques enable a rollback to the previous state of the game [4, 5]. | + | |
- | • Predictive techniques estimate the occurring events in the game from the locally available state [4, 5]. | + | |
- | The goal of this work is to build a predictive | + | The goal of this work is to build a predictive |
- | This work aims to build and evaluate a predictive system for the video game Counter Strike: Global Offensive. | + | This work aims to build and evaluate a predictive system |
=== Zielsetzung der Arbeit === | === Zielsetzung der Arbeit === | ||
+ | Firstly a suitable way to collect data from CS:GO needs to be implemented. There a two possible ways to collect data: (1) Directly log user input and game screen while live-playing and (2) use openly availabe CS:GO replays. Both approaches need to be evaluated and analyzed regarding their feasablity for this works aim. | ||
- | The first step of the work is creating | + | After collecting |
- | After training the ANN, it is evaluated in various scenarios. The goal is to determine if the developed predictive system is practicable | + | After training the ANN, it is evaluated in various scenarios. The goal is to determine if the developed predictive system is suitable for latency compensation |
=== Konkrete Aufgaben === | === Konkrete Aufgaben === | ||
- | • Literature research | + | * Literature research |
- | + | * Create a suitable dataframe for ANN training | |
- | • Create a suitable dataframe for ANN training | + | * Train the latency compensation model |
- | + | * Evaluate the developed model | |
- | • Train the latency compensation model | + | * Write the thesis |
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- | • Evaluate the developed model | + | |
- | | + | |
=== Erwartete Vorkenntnisse === | === Erwartete Vorkenntnisse === | ||
- | • Tensorflow, Pytorch or Keras | + | * Tensorflow, Pytorch or Keras |
- | • Programming in C++/Python and Unity | + | * Programming in C++/Python and Unity |
- | • Data analysis | + | * Data analysis |
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[1] Latency. (2021). Wikipedia. https:// | [1] Latency. (2021). Wikipedia. https:// | ||
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[2] Claypool, M., & Claypool, K. (2006). Latency and player actions in online games. Communications of the ACM, 49(11), 40-45. | [2] Claypool, M., & Claypool, K. (2006). Latency and player actions in online games. Communications of the ACM, 49(11), 40-45. | ||
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[3] Claypool, M., & Claypool, K. (2010, February). Latency can kill: precision and deadline in online games. In Proceedings of the first annual ACM SIGMM conference on Multimedia systems (pp. 215-222). | [3] Claypool, M., & Claypool, K. (2010, February). Latency can kill: precision and deadline in online games. In Proceedings of the first annual ACM SIGMM conference on Multimedia systems (pp. 215-222). | ||
- | [4] Savery, C., & Graham, T. N. (2013). Timelines: simplifying the programming of lag compensation for the next generation of networked games. Multimedia Systems, 19(3), 271-287. | + | |
- | [5] Sabet, S. S., Schmidt, S., Zadtootaghaj, | + | [4] Niels Henze, Markus Funk, and Alireza Sahami Shirazi. 2016. Software-reduced touchscreen latency. In Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services. 434–441. |
- | [6] Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, August). Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET) (pp. 1-6). Ieee. | + | |
- | [7] Cai, E., Juan, D. C., Stamoulis, D., & Marculescu, D. (2019). Learning-based Power and Runtime Modeling for Convolutional Neural Networks. | + | [5] Savery, C., & Graham, T. N. (2013). Timelines: simplifying the programming of lag compensation for the next generation of networked games. Multimedia Systems, 19(3), 271-287. |
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+ | [6] Sabet, S. S., Schmidt, S., Zadtootaghaj, | ||
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+ | [7] Valentin Schwind, David Halbhuber, Jakob Fehle, Jonathan Sasse, Andreas Pfaffelhuber, | ||
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+ | [8] Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, August). Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET) (pp. 1-6). Ieee. | ||
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+ | [9] Cai, E., Juan, D. C., Stamoulis, D., & Marculescu, D. (2019). Learning-based Power and Runtime Modeling for Convolutional Neural Networks. | ||