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Abstract. One of the key properties of (natural) languages is that they are hierarchical. Phrases combine into larger phrases eventually covering complete sentences. The semantics of each phrase combine to form the complex meaning of the whole. A key question in explaining the origins and evolution of language is therefore how such hierarchical structures may originate and spread in a population. This paper reports on breakthrough experiments in which unbounded hierarchical structure can emerge as a side-effect of repair strategies in a population of agents. 1

It is well known that perspective alignment plays a major role in the planning and interpretation of spatial language. In order to understand the role of perspective alignment and the cognitive processes involved, we have made precise complete cognitive models of situated embodied agents that self-organise a communication system for dialoging about the position and movement of real world objects in their immediate surroundings. We show in a series of robotic experiments which cognitive mechanisms are necessary and sufficient to achieve successful spatial language and why and how perspective alignment can take place, either implicitly or based on explicit marking. 1

This paper illustrates how external (or 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 inter-actions 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 sym-bol grounding problem is a particularly hard one; however, we provide an ideal platform to study this problem.

Within a computer vision context color naming is the action of assigning linguistic color labels to image pixels. In general, research on color naming applies the following paradigm: a collection of color chips is labelled with color names within a well-defined experimental setup by multiple test subjects. The collected data set is subsequently used to label RGB values in real-world images with a color name. Apart from the fact that this collection process is time consuming, it is unclear to what extent color naming within a controlled setup is representative for color naming in realworld images. Therefore we propose to learn color names from real-world images. Furthermore, we avoid test subjects by using Google Image to collect a data set. Due to limitations of Google Image this data set contains a substantial quantity of wrongly labelled data. The color names are learned using a PLSA model adapted to this task. Experimental results show that color names learned from realworld images significantly outperform color names learned from labelled color chips on retrieval and classification. 1.

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This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: (i) how agents learn and represent compositional actions; (ii) how agents learn and represent compositional lexicons; (iii) the dynamics of social interaction and learning; and (iv) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test-scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.

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.

Taking a distributed view of language, this paper naturalizes symbol grounding. Learning to talk is traced to – not categorizing speech sounds – but events that shape the rise of human-style autonomy. On the extended symbol hypothesis, this happens as babies integrate micro-activity with slow and deliberate adult action. As they discover social norms, intrinsic motive formation enables them to reshape co-action. Because infants link affect to contingencies, dyads develop norm-referenced routines. Over time, infant doings become analysis amenable. The caregiver of a nine-month-old may, for example, prompt the baby to fetch objects. Once she concludes that the baby uses ‘words ’ to understand what she says, the infant can use this belief in orienting to more abstract contingencies. New cognitive powers will develop as the baby learns to act in ways that are consistent with a caregiver’s false belief that her baby uses ‘words.’

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