Language Evolution and Computation Bibliography

This paper illustrates how external (or {\em social}) 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.

In this paper we introduce a model for the simulation of language evolution, which is incorporated in the New Ties project. The New Ties project aims at evolving a cultural society by integrating evolutionary, individual and social learning in large scale multi-agent simulations. The model presented here introduces a novel implementation of language games, which allows agents to communicate in a more natural way than with most other existing implementations of language games. In particular, we propose a hybrid mechanism that combines cross-situational learning techniques with more informed feedback mechanisms. In our study we focus our attention on dealing with referential indeterminacy after joint attention has been established and on whether the current model can deal with larger populations than previous studies involving cross-situational learning. Simulations show that the proposed model can indeed lead to coherent languages in a quasi realistic world environment with larger populations.

Typically, multi-agent models for studying the evolution of perceptually grounded lexicons assume that agents perceive the same set of objects, and that there is either joint attention, corrective feedback or cross-situational learning. In this paper we address these two assumptions, by introducing a new multi-agent model for the evolution of perceptually grounded lexicons, where agents do not perceive the same set of objects, and where agents receive a cue to focus their attention to objects, thus simulating a Theory of Mind. In addition, we vary the amount of corrective feedback provided to guide learning word-meanings. Results of simulations show that the proposed model is quite robust to the strength of these cues and the amount of feedback received.

In this paper we discuss issues relating to modelling language evolution in large populations of autonomous agents that are situated in a realistic environment where they have to evolve and learn means to survive for extended periods of time. As we intend to build such a model in relation to the recently started New Ties project, we identify three major problems that are expected for such a model. The paper proposes some solutions and discusses future directions.