Automated Semantic Rules Detection (ASRD) for Emergent Communication Interpretation

TL;DR

This paper introduces ASRD, an algorithm that automatically detects semantic patterns in emergent communication, aiding interpretability and analysis of agent-developed languages in multi-agent systems.

Contribution

The paper presents a novel Automated Semantic Rules Detection algorithm that enhances understanding of emergent languages by extracting meaningful patterns from agent communication.

Findings

ASRD successfully identifies relevant semantic patterns.

The method improves interpretability of emergent communication.

Applied to Lewis Game datasets, it simplifies analysis.

Abstract

The field of emergent communication within multi-agent systems examines how autonomous agents can independently develop communication strategies, without explicit programming, and adapt them to varied environments. However, few studies have focused on the interpretability of emergent languages. The research exposed in this paper proposes an Automated Semantic Rules Detection (ASRD) algorithm, which extracts relevant patterns in messages exchanged by agents trained with two different datasets on the Lewis Game, which is often studied in the context of emergent communication. ASRD helps at the interpretation of the emergent communication by relating the extracted patterns to specific attributes of the input data, thereby considerably simplifying subsequent analysis.