We present a method for learning to interpret and understand foreign agent communication. Our approach is based on casting the contents of intercepted opponent agent communication to a bit-level representation and on training and employing deep convolutional neural networks for decoding the meaning of received messages. We empirically evaluate our method on real-world data acquired from the multi-agent domain of robotic soccer simulation, demonstrating the effectiveness and robustness of the learned decoding models.
CITATION STYLE
Gabel, T., Tharwat, A., & Godehardt, E. (2017). Eavesdropping opponent agent communication using deep learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10413 LNAI, pp. 205–222). Springer Verlag. https://doi.org/10.1007/978-3-319-64798-2_13
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