2024/01/16 by Jérôme Botoko Ekila, Lara Verheyen, Ekila, Jérôme Botoko +7
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Language and cultural evolution #Neural and Evolutionary Computing (cs.NE) #Speech and dialogue systems #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2401.08461
openalex publication_date 2024/01/16 · openalex created_date 2024/01/18 · openalex updated_date 2026/08/01
This paper introduces a general methodology through which a population of autonomous agents can converge on a linguistic convention that enables them to refer to arbitrary entities in their environment. The linguistic convention emerges in a decentralised manner through local communicative interactions between pairs of agents drawn from the population. The emergent convention consists of associations between symbolic labels (word forms) and subsymbolic concept representations (word meanings) that are grounded in a continuous feature space. We confirm the generality and scalability of the method through its evaluation on a wide and diverse selection of 37 publicly available datasets. Through a range of experiments, we demonstrate the robustness of the method against perceptual variation, including in heteromorphic populations, as well as the ability of the emergent conventions to self-adapt to changes in the environment.