Humans maintain a body image of themselves, which plays a central role in controlling bodily movement, planning action, recognising and naming actions performed by others, and requesting or executing commands. This paper explores through experiments with autonomous humanoid robots how such a body image could form. Robots play a situated embodied language game called the Action Game in which they ask each other to perform bodily actions. They start without any prior inventory of names, without categories for visually recognising body movements of others, and without knowing the relation between visual images of motor behaviours carried out by others and their own motor behaviours. Through diagnostic and repair strategies carried out within the context of action games, they progressively self-organise an effective lexicon as well as bi-directional mappings between the visual and the motor domain. The agents thus establish and continuously adapt networks linking perception, body representation, action and language.

Learning the meanings of words requires coping with referential uncertainty – a learner hearing a novel word cannot be sure which aspects or properties of the referred object or event comprise the meaning of the word. Data from developmental psychology suggest that human learners grasp the important aspects of many novel words after just a few exposures, a phenomenon known as fast mapping. Traditionally, word learning is viewed as a mapping task, in which the learner has to map a set of forms onto a set of pre-existing concepts. We criticise this approach and argue instead for a flexible nature of the coupling between form and meanings as a solution to the problem of referential uncertainty. We implemented and tested the model in populations of humanoid robots that play situated language games about objects in their shared environment. Results show that the model can handle an exponential increase in uncertainty and allows scaling towards very large meaning spaces, while retaining the ability to grasp an operational meaning almost instantly for a great number of words. In addition, the model captures some aspects of the flexibility of form-meaning associations found in human languages. Meanings of words can shift between being very specific (names) and general (e.g. ‘small’). We show that this specificity is biased not by the model itself but by the distribution of object properties in the world.

The paper focuses on the problem how a community of distributed agents may autonomously invent and coordinate lexicons and grammars. Although our earlier experiments have shown that a communication system can indeed emerge in a socio-cultural dynamics, it relies on the control of complexity by the experimenter, so that agents first acquire words, then simple constructions, and then more complex ones. This paper addresses the question how agents could themselves regulate the complexity both of the mechanisms they bring to bear to the language task and on the semantic complexity of what they want to express. We make use of the autotelic principle, coming from psychology. It requires monitoring challenge and skill (based on actual performance) and maintaining a 'flow' regime balancing the two. We show in computational experiments that the autotelic principle is able to explain autonomous scaffolding towards greater complexity in the emergence of language.

Summary form only given. Human language and natural dialogue are not based on a set of static conventions. Human languages and interaction patterns evolve, because humans must constantly invent, adapt, and mold their communication systems and conceptualisations of the world in order to cope with an open-ended set of tasks and environments that can often not be constrained or entirely known in advance. Is it possible to endow robots with the necessary cognitive mechanisms and interaction scripts that they would have the same capacities for open-ended communication? In other words, is it possible that robots can be programmed to participate in the creative invention and adaptation of their interfaces with humans or other robots?

In this paper, efforts to understand the self-organization and evolution of language from a cognitive modelling point of view are discussed. In particular, the paper focuses on efforts that use connectionist components to synthesize some of the major stages in the emergence of language and possible transitions between stages. New technical results are not introduced, but some dimensions for mapping out the research landscape are discussed.

The paper reports on progress in building computational models of a constructivist approach to language development. It introduces a formalism for construction grammars and learning strategies based on invention, abduction, and induction. Examples are drawn from experiments exercising the model in situated language games played by embodied artificial agents.

Language is a shared set of conventions for mapping meanings to utterances. This paper explores self-organization as the primary mechanism for the formation of a vocabulary. It reports on a computational experiment in which a group of distributed agents develop ways to identify each other using names or spatial descriptions. It is also shown that the proposed mechanism copes with the acquisition of an existing vocabulary by new agents entering the community and with an expansion of the set of meanings.