From Grunts to Grammar: Emergent Language from Cooperative Foraging

2025-05-19

AI Summary

This study investigates how language may have spontaneously emerged in early humans due to cooperative survival pressures. Method: Using a partially observable multi-agent foraging environment, we employ end-to-end deep reinforcement learning (Proximal Policy Optimization, PPO) to enable agents to co-evolve communication protocols from scratch without pre-defined linguistic constraints. Contribution/Results: We present the first systematic quantification—within an embodied multi-agent setting—of five core linguistic properties of emergent languages: arbitrariness, interchangeability, displacement, cultural transmission, and compositionality. Our experiments causally demonstrate how population size and evolutionary duration shape linguistic structure. Agents spontaneously develop communication protocols exhibiting key hallmarks of natural language. To foster reproducibility and advance research on language origins, we release all code, models, and data, establishing the first open, reproducible multi-agent simulation platform dedicated to the study of language evolution.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Evolutionary Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Early cavemen relied on gestures, vocalizations, and simple signals to coordinate, plan, avoid predators, and share resources. Today, humans collaborate using complex languages to achieve remarkable results. What drives this evolution in communication? How does language emerge, adapt, and become vital for teamwork? Understanding the origins of language remains a challenge. A leading hypothesis in linguistics and anthropology posits that language evolved to meet the ecological and social demands of early human cooperation. Language did not arise in isolation, but through shared survival goals. Inspired by this view, we investigate the emergence of language in multi-agent Foraging Games. These environments are designed to reflect the cognitive and ecological constraints believed to have influenced the evolution of communication. Agents operate in a shared grid world with only partial knowledge about other agents and the environment, and must coordinate to complete games like picking up high-value targets or executing temporally ordered actions. Using end-to-end deep reinforcement learning, agents learn both actions and communication strategies from scratch. We find that agents develop communication protocols with hallmark features of natural language: arbitrariness, interchangeability, displacement, cultural transmission, and compositionality. We quantify each property and analyze how different factors, such as population size and temporal dependencies, shape specific aspects of the emergent language. Our framework serves as a platform for studying how language can evolve from partial observability, temporal reasoning, and cooperative goals in embodied multi-agent settings. We will release all data, code, and models publicly.

Problem

• How language emerges from cooperative foraging in multi-agent systems
• Analyzing properties of emergent communication in deep reinforcement learning
• Studying language evolution under ecological and social constraints

Innovation

• Multi-agent Foraging Games simulate early human cooperation
• End-to-end deep reinforcement learning for communication strategies
• Agents develop language with natural language features