SimSiam Naming Game: A Unified Approach for Emergent Communication and Representation Learning
arXiv:2410.21803v3 Announce Type: replace Abstract: Emergent Communication (EmCom) investigates how agents develop symbolic communication through interaction without predefined language. Recent frameworks, such as the Metropolis--Hastings Naming Game (MHNG), formulate EmCom as the learning of shared external representations negotiated through interaction under joint attention, without explicit success or reward feedback. However, MHNG relies on sampling-based updates that suffer from high rejection rates in high-dimensional perceptual spaces, making the learning process sample-inefficient for
The paper addresses current limitations in emergent communication research by proposing a more efficient learning framework, pushing the boundaries of autonomous agent interaction and representation learning.
Improved emergent communication and representation learning are critical for developing more sophisticated and adaptable AI agents that can operate without explicit human programming or supervision.
The proposed 'SimSiam Naming Game' offers a method to overcome limitations in existing emergent communication frameworks, potentially accelerating the development of more robust and sample-efficient agent communication systems.
- · AI Agent Developers
- · Robotics Research
- · Generative AI
- · AI systems reliant on predefined communication
More efficient training methods for AI to develop shared understanding and communication protocols.
Accelerated deployment of autonomous AI agents capable of complex, collaborative tasks.
The development of truly 'understanding' AI that can adapt to novel communication contexts and tasks without human intervention, leading to new forms of human-AI collaboration.
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