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

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Summary
This summary is machine-generated.

This study introduces an information-constrained framework for emergent communication, balancing utility, informativeness, and complexity. The Vector-Quantized Variational Information Bottleneck (VQ-VIB) method enhances learning and generalization, producing more human-like language in AI agents.

Keywords:
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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.