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.

Constructionist approaches to language posit that all linguistic knowledge is captured in constructions. These constructions pair form and meaning at varying levels of abstraction, ranging from purely substantive to fully abstract and are all acquired through situated communicative interactions. In this paper we provide computational support for these foundational principles. We present a model that enables an artificial learner agent to acquire a construction grammar directly from its sensory experience. The grammar is built from the ground up, i.e. without a given lexicon, predefined categories or ontology and covers a range of constructions, spanning from purely substantive to partially schematic. Our approach integrates two previously separate but related experiments, allowing the learner to incrementally build a linguistic inventory that solves a question-answering task in a synthetic environment. These findings demonstrate that linguistic knowledge at different levels can be mechanistically acquired from experience.