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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.15985 |
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| _version_ | 1866909353674539008 |
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| author | Chen, Hang Jang, Yuchuan Zhou, Weijie Meo, Cristian Chen, Ziwei Liu, Dianbo |
| author_facet | Chen, Hang Jang, Yuchuan Zhou, Weijie Meo, Cristian Chen, Ziwei Liu, Dianbo |
| contents | Individuals, despite having varied life experiences and learning processes, can communicate effectively through languages. This study aims to explore the efficiency of language as a communication medium. We put forth two specific hypotheses: First, discrete messages are more effective than continuous ones when agents have diverse personal experiences. Second, communications using multiple discrete tokens are more advantageous than those using a single token. To valdate these hypotheses, we designed multi-agent machine learning experiments to assess communication efficiency using various information transmission methods between speakers and listeners. Our empirical findings indicate that, in scenarios where agents are exposed to different data, communicating through sentences composed of discrete tokens offers the best inter-agent communication efficiency. The limitations of our finding include lack of systematic advantages over other more sophisticated encoder-decoder model such as variational autoencoder and lack of evluation on non-image dataset, which we will leave for future studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_15985 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Discrete Messages Improve Communication Efficiency among Isolated Intelligent Agents Chen, Hang Jang, Yuchuan Zhou, Weijie Meo, Cristian Chen, Ziwei Liu, Dianbo Machine Learning Information Theory Individuals, despite having varied life experiences and learning processes, can communicate effectively through languages. This study aims to explore the efficiency of language as a communication medium. We put forth two specific hypotheses: First, discrete messages are more effective than continuous ones when agents have diverse personal experiences. Second, communications using multiple discrete tokens are more advantageous than those using a single token. To valdate these hypotheses, we designed multi-agent machine learning experiments to assess communication efficiency using various information transmission methods between speakers and listeners. Our empirical findings indicate that, in scenarios where agents are exposed to different data, communicating through sentences composed of discrete tokens offers the best inter-agent communication efficiency. The limitations of our finding include lack of systematic advantages over other more sophisticated encoder-decoder model such as variational autoencoder and lack of evluation on non-image dataset, which we will leave for future studies. |
| title | Discrete Messages Improve Communication Efficiency among Isolated Intelligent Agents |
| topic | Machine Learning Information Theory |
| url | https://arxiv.org/abs/2312.15985 |