backpropper/cbc-emecom
Code repository for Capacity, Bandwidth, and Compositionality in Emergent Language Learning (AAMAS 2020)
This project helps researchers understand how artificial agents develop communication systems. It takes parameters like message complexity and concept count as input, and outputs models and analysis about the emergent languages formed. Anyone studying multi-agent systems, artificial intelligence, or the origins of communication could use this.
No commits in the last 6 months.
Use this if you are an AI researcher or cognitive scientist interested in experimenting with the factors that influence how artificial agents learn to communicate effectively.
Not ideal if you're looking for an off-the-shelf natural language processing tool or a system for human-computer interaction.
Stars
7
Forks
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Language
Python
License
MIT
Category
Last pushed
May 15, 2020
Commits (30d)
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