DESEM: Depthwise Separable Convolution-Based Multimodal Deep Learning for In-Game Action Anticipation

DESEM: Depthwise Separable Convolution-Based Multimodal Deep Learning for In-Game Action Anticipation

In real-time strategy (RTS) games, to defeat their opponents, players need to choose and implement the correct sequential actions. Because RTS games like StarCraft II are real-time, players have a very limited time to choose how to develop their strategy. In addition, players can only partially obse...

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Bibliographic Details
Main Authors: Bae, J. (Author), Baek, I. (Author), Jeong, J. (Author), Kim, C. (Author), Kim, S.B (Author), Lee, Y.J (Author), Park, K. (Author), Shim, S.H (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:
action anticipation
Artificial intelligence
Convolutional neural networks
Deep learning
depthwise separable convolution
Feature extraction
Forecasting
game artificial intelligence
Games
multimodal deep learning
Videos
weighted loss function
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