What kind of motivation drives child language development? This article presents a computational model and a robotic experiment to articulate the hypothesis that children discover communication as a result of exploring and playing with their environment. The considered robotic agent is intrinsically motivated towards situations in which it optimally progresses in learning. To experience optimal learning progress, it must avoid situations already familiar but also situations where nothing can be learnt. The robot is placed in an environment in which both communicating and non-communicating objects are present. As a consequence of its intrinsic motivation, the robot explores this environment in an organized manner focusing first on non-communicative activities and then discovering the learning potential of certain types of interactive behaviour. In this experiment, the agent ends up being interested by communication through vocal interactions without having a specific drive for communication.

Computational models have played a central role in the debate over language learnability. This article discusses how they have been used in different “stances”, from generative views to more recently introduced explanatory frameworks based on embodiment, cognitive development and cultural evolution. By digging into the details of certain specific models, we show how they organize, transform and rephrase defining questions about what makes language learning possible for children. Finally, we present a tentative synthesis to recast the debate using the notion of learning bias.

Over the past years, several computational models have been introduced to study the coordination of communication between distributed agents. Although these models have given valuable insights into the mechanisms required for letting agents develop a successful communication system, few theoretical results have been obtained which substantiate these findings. In this paper we introduce a theoretical framework which allows us to analyze and compare different existing models in a uniform way. Therefore we only look at the observable behavior of an agent and not at the internal mechanisms that cause that behavior. In particular, we define an agent’s response function and argue that a stability analysis of its fixed points reveals crucial information about the convergence properties of the dynamical system of interacting agents. 1

We study the emergence of shared representations in a population of agents engaged in a supervised classification task, using a model called the Classification Game. We connect languages with tasks by treating the agents ’ classification hypothesis space as an information channel. We show that by learning through the classification game, agents can implicitly perform complexity regularization, which improves generalization. Improved generalization also means that the languages that emerge are well-adapted to the given task. The improved language-task fit springs from the interplay of two opposing forces: the dynamics of collective learning impose a preference for simple representations, while the intricacy of the classification task imposes a pressure towards representations that are more complex. The push-pull of these two forces results in the emergence of a shared representation that is simple but not too simple. Our agents use artificial neural networks to solve the classification tasks they face, and a simple counting algorithm to learn a language as a form-meaning mapping. We present several experiments to demonstrate that both compositional and holistic languages can emerge in our system. We also demonstrate that the agents avoid overfitting on noisy data, and can learn some very difficult tasks through interaction, which they are unable to learn individually. Further, when the agents use simple recurrent networks to solve temporal classification tasks, we see the emergence of a rudimentary grammar, which does not have to be explicitly learned.

development in robotic systems (2008)"