A framework for the emergence and analysis of language in social learning agents
Artificial neural networks (ANNs) are increasingly used as research models, but questions remain about their generalizability and representational invariance. Biological neural networks under social constraints evolved to enable communicable representations, demonstrating generalization capabilities...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
04.05.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Artificial neural networks (ANNs) are increasingly used as research models,
but questions remain about their generalizability and representational
invariance. Biological neural networks under social constraints evolved to
enable communicable representations, demonstrating generalization capabilities.
This study proposes a communication protocol between cooperative agents to
analyze the formation of individual and shared abstractions and their impact on
task performance. This communication protocol aims to mimic language features
by encoding high-dimensional information through low-dimensional
representation. Using grid-world mazes and reinforcement learning, teacher ANNs
pass a compressed message to a student ANN for better task completion. Through
this, the student achieves a higher goal-finding rate and generalizes the goal
location across task worlds. Further optimizing message content to maximize
student reward improves information encoding, suggesting that an accurate
representation in the space of messages requires bi-directional input. This
highlights the role of language as a common representation between agents and
its implications on generalization capabilities. |
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DOI: | 10.48550/arxiv.2305.02632 |