development

Abstract not found

This paper presents arguments for approaching the anchoring problem using semiotic symbols. Semiotic symbols are de ned by a triadic relation between forms, meanings and referents, thus having an implicit relation to the real world. Anchors are formed between these three elements rather than between `traditional' symbols and sensory images. This allows an optimization between the form (i.e. the `traditional' symbol) and the referent. A robotic experiment based on adaptive language games illustrates how the anchoring of semiotic symbols can be achieved in a bottom-up fashion. The paper concludes that applying semiotic symbols is a potentially valuable approach toward anchoring.

Abstract. We present an artificial cognitive system for learning visual concepts. It comprises of vision, communication and manipulation subsystems, which provide visual input, enable verbal and non-verbal communication with a tutor and allow interaction with a given scene. The main goal is to learn associations between automatically extracted visual features and words that describe the scene in an open-ended, continuous manner. In particular, we address the problem of cross-modal learning of visual properties and spatial relations. We introduce and analyse several learning modes requiring different levels of tutor supervision. 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.

The success of social robots in achieving natural coexistence with humans depends on both their level of autonomy and their interactive abilities. Although a lot of robotic architectures have been suggested and many researchers have focused on human-robot interaction, a robotic architecture that can effectively combine interactivity and autonomy is still unavailable. This paper contributes to the research efforts towards this architecture in the following ways. First a theoretical analysis is provided that leads to the notion of co-evolution between the agent and its environment and with other agents as the condition needed to combine both autonomy and interactivity. The analysis also shows that the basic competencies needed to achieve the required level of autonomy and the envisioned level of interactivity are similar but not the same. Secondly nine specific requirements are then formalized that should be achieved by the architecture. Thirdly a robotic architecture that tries to achieve those requirements by utilizing two main theoretical hypothesis and several insights from social science, developmental psychology and neuroscience is detailed. Lastly two experiments with a humanoid robot and a simulated agent are reported to show the potential of the proposed architecture.

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This paper investigates how robots may emerge a lexicon to communicate complex meanings about actions such as `I am going to the red target' using simple (one-word) utterances. The main issue of the paper concerns the way these complex meanings represent the actions that are performed. It is argued that the meaning of these utterances may be represented without the need for categorising a complex ow of sensorimotor data. To illustrate the point, a simulation is presented in which robots develop such a communication system. The paper concludes by con rming that it is well possible to construct such a lexicon once robots have a number of basic sensorimotor skills available.

this paper we are concerned with the pragmatic of contextual cues that indicate the meaning of spoken words. Such cues may, for instance, be provided by establishing joint attention or by evaluating corrective feedback, but there is some evidence that children do not need such directed cues to learn the meaning of their rst words (Lieven, 1994)

The success of social robots in achieving natural coexistence with humans depends on both their level of autonomy and their interactive abilities. Although many robotic architectures have been suggested and many researchers have focused on human-robot interaction, a robotic architecture that can effectively combine interactivity and autonomy is still unavailable. In this paper a robotic architecture called EICA (Embedded Interactive Control Architecture) is presented that tries to fill this gap. The proposed architecture can help robotic designers in creating sociable robots that can combine both natural interactivity with humans and reactive fast response to the physical and social environmental changes. The resulting robots can then be more socially intelligent than the robots designed using current deliberative, reactive or even hybrid architectures.