This paper illustrates how external symbol grounding can be studied in simulations with large populations. We discuss how we can simulate language evolution in a relatively complex environment which has been developed in the context of the New Ties project. This project has the objective of evolving a cultural society and, in doing so, the agents have to evolve a communication system that is grounded in their interactions with their virtual environment and with other individuals. A preliminary experiment is presented (...) in which we investigate the effect of a number of learning mechanisms. The results show that the social symbol grounding problem is a particularly hard one; however, we provide an ideal platform to study this problem. (shrink)

This paper illustrates how external symbol grounding can be studied in simulations with large populations. We discuss how we can simulate language evolution in a relatively complex environment which has been developed in the context of the New Ties project. This project has the objective of evolving a cultural society and, in doing so, the agents have to evolve a communication system that is grounded in their interactions with their virtual environment and with other individuals. A preliminary experiment is presented (...) in which we investigate the effect of a number of learning mechanisms. The results show that the social symbol grounding problem is a particularly hard one; however, we provide an ideal platform to study this problem. (shrink)

Cross-situational learning has recently gained attention as a plausible candidate for the mechanism that underlies the learning of word-meaning mappings. In a recent study, Blythe and colleagues have studied how many trials are theoretically required to learn a human-sized lexicon using cross-situational learning. They show that the level of referential uncertainty exposed to learners could be relatively large. However, one of the assumptions they made in designing their mathematical model is questionable. Although they rightfully assumed that words are distributed according (...) to Zipf's law, they applied a uniform distribution of meanings. In this article, Zipf's law is also applied to the distribution of meanings, and it is shown that under this condition, cross-situational learning can only be plausible when referential uncertainty is sufficiently small. It is concluded that cross-situational learning is a plausible learning mechanism but needs to be guided by heuristics that aid word learners with reducing referential uncertainty. (shrink)

Research into child language reveals that it takes a long time for children to learn the correct mapping of colour words. Steels & Belpaeme's (S&B's) guessing game, however, models fast learning of words. We discuss computational studies based on cross-situational learning, which yield results that are more consistent with the empirical child language data than those obtained by S&B.