2024/09/11 by Andrea Silvi, Jonathan Thomas, Silvi, Andrea +7
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Neural Networks and Applications #Reinforcement Learning in Robotics
paper · pdf · doi:10.48550/arxiv.2409.07170
openalex publication_date 2024/09/11 · openalex created_date 2024/10/22 · openalex updated_date 2026/07/28
It has previously been shown that by using reinforcement learning (RL), agents can derive simple approximate and exact-restricted numeral systems that are similar to human ones (Carlsson, 2021). However, it is a major challenge to show how more complex recursive numeral systems, similar to for example English, could arise via a simple learning mechanism such as RL. Here, we introduce an approach towards deriving a mechanistic explanation of the emergence of efficient recursive number systems. We consider pairs of agents learning how to communicate about numerical quantities through a meta-grammar that can be gradually modified throughout the interactions. Utilising a slightly modified version of the meta-grammar of Hurford (1975), we demonstrate that our RL agents, shaped by the pressures for efficient communication, can effectively modify their lexicon towards Pareto-optimal configurations which are comparable to those observed within human numeral systems in terms of their efficiency.