The paper presents a novel approach called Abstractions for Communicating Efficiently (ACE) that integrates techniques from emergent communication, library learning, and bandit algorithms to enable artificial agents to develop a more efficient language for collaborative problem-solving.
The key components of ACE are:
Abstraction: The architect agent identifies new abstractions, which are common sub-sequences within programs, and introduces them into the shared language through a library learning mechanism.
Communication: The architect and builder agents use emergent communication techniques to learn a language for collaboration, which is subject to pressures for efficient communication.
Exploration/Exploitation: Bandit techniques are used to control the trade-off between exploring new abstractions and exploiting the current language, allowing the agents to gradually introduce and learn to use the most informative abstractions.
ACE is evaluated on an extended version of the Architect-Builder game, where one agent (the architect) instructs the other (the builder) to reconstruct a scene of block-buildings. The results show that ACE exhibits similar tendencies to humans, developing a more concise language over repeated interactions. The introduced abstractions are subject to pressures for efficient communication, with the final language retaining only the most useful abstractions.
This work represents a step towards equipping intelligent agents with the ability to flexibly extend their communication capabilities through the introduction of abstractions, which can facilitate improved cooperation between humans and agents.
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arxiv.org
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by Jonathan D. ... at arxiv.org 10-01-2024
https://arxiv.org/pdf/2409.20120.pdfDeeper Inquiries