Towards Human-Like Emergent Communication via Utility, Informativeness, and Complexity

Area of Science:

• Computational Linguistics
• Artificial Intelligence
• Information Theory

Background:

• Contrasting theories of language evolution exist: task-specific utility maximization versus task-agnostic communicative efficiency.
• Communicative efficiency is often explained by the Information Bottleneck (IB) principle, balancing complexity and informativeness.
• Existing models often focus on one aspect, neglecting the interplay between utility, informativeness, and complexity.

Purpose of the Study:

• To integrate task-specific utility and task-agnostic communicative efficiency into a unified framework for emergent communication.
• To develop a novel training method, Vector-Quantized Variational Information Bottleneck (VQ-VIB), for information-constrained emergent communication.
• To evaluate the impact of information constraints on learning speed, generalization, and human-like language properties in artificial agents.

Main Methods:

• Proposed an information-constrained emergent communication framework balancing utility, informativeness, and complexity.
• Developed the Vector-Quantized Variational Information Bottleneck (VQ-VIB) method for training agents with discrete communication in a continuous vector space.
• Tested the VQ-VIB approach across three distinct domains to assess its performance.

Main Results:

• Agents trained with VQ-VIB demonstrated faster learning and improved generalization to new domains when incentivized for informativeness.
• Limiting communication complexity led to emergent communication patterns more aligned with human languages.
• VQ-VIB outperformed existing emergent communication methods, attributed to its semantically meaningful communication embedding space.

Conclusions:

• Cognitively-motivated optimality principles, like those in the IB principle, are crucial for inducing human-like communication in artificial agents.
• The proposed framework and VQ-VIB method offer a promising direction for developing more sophisticated and human-like AI communication systems.
• Balancing utility, informativeness, and complexity provides a more comprehensive approach to understanding and replicating language evolution in AI.