The Emergence of Grammar through Reinforcement Learning

2025-03-03

AI Summary

This study investigates the functionalist hypothesis that speakers’ communicative intentions drive syntactic evolution. To this end, it introduces reinforcement learning (RL) into grammatical evolution modeling for the first time, proposing a “message-probability-driven stepwise learning mechanism” to simulate the incremental acquisition of syntactic and semantic composition rules. Methodologically, it integrates probabilistic modeling, numerical simulation, formal language analysis, and empirical historical linguistics—combining analytical derivation with empirical validation. Theoretically, it establishes rigorous mathematical conditions for the emergence of grammatical structure. Methodologically, it bridges formal linguistics and RL. Empirically, it successfully replicates multiple diachronic pathways and validates the model’s explanatory power against two classic cases in English historical linguistics—namely, the decay of the case system and the evolution of auxiliaries—thereby providing a computationally tractable and empirically testable functionalist framework for language change.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Evolutionary Learning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

The evolution of grammatical systems of syntactic and semantic composition is modeled here with a novel application of reinforcement learning theory. To test the functionalist thesis that speakers' expressive purposes shape their language, we include within the model a probability distribution over different messages that could be expressed in a given context. The proposed learning and production algorithm then breaks down language learning into a sequence of simple steps, such that each step benefits from the message probabilities. The results are presented in the form of numerical simulations of language histories and analytic proofs. The potential for applying these mathematical models to the study of natural language is illustrated with two case studies from the history of English.

Problem

• Modeling evolution of grammatical systems using reinforcement learning.
• Testing how speakers' expressive purposes shape language structure.
• Applying mathematical models to study natural language history.

Innovation

• Reinforcement learning models grammatical evolution
• Message probability distribution guides language learning
• Numerical simulations and proofs validate language models