  This paper introduces a methodology through which a population of autonomous
agents can establish a linguistic convention that enables them to refer to
arbitrary entities that they observe in their environment. The linguistic
convention emerges in a decentralised manner through local communicative
interactions between pairs of agents drawn from the population. The convention
consists of symbolic labels (word forms) associated to concept representations
(word meanings) that are grounded in a continuous feature space. The concept
representations of each agent are individually constructed yet compatible on a
communicative level. Through a range of experiments, we show (i) that the
methodology enables a population to converge on a communicatively effective,
coherent and human-interpretable linguistic convention, (ii) that it is
naturally robust against sensor defects in individual agents, (iii) that it can
effectively deal with noisy observations, uncalibrated sensors and
heteromorphic populations, (iv) that the method is adequate for continual
learning, and (v) that the convention self-adapts to changes in the environment
and communicative needs of the agents.
