The paper proposes a number of models to examine through what mech-anisms a population of autonomous agents could arrive at a repertoire of perceptually grounded categories that is sufficiently shared to allow successful communication. The models are inspired by the main approaches to human categorisation being discussed in the literature: nativism, empiricism, and culturalism. Colour is taken as a case study. Although the paper takes no stance on which position is to be accepted as final truth with respect to hu-man categorisation and naming, it points to theoretical constraints that make each position more or less likely and contains clear suggestions on what the best engineering solution would be. Specifically, it argues that the collective choice of a shared repertoire must integrate multiple constraints, including constraints coming from communication.

This paper presents an approach to solve the symbol grounding problem within the framework of embodied cognitive science. It will be argued that symbolic structures can be used within the paradigm of embodied cognitive science by adopting an alternative definition of a symbol. In this alternative definition, the symbol may be viewed as a structural coupling between an agent's sensorimotor activations and its environment. A robotic experiment is presented in which mobile robots develop a symbolic structure from scratch by engaging in a series of language games. In this experiment it is shown that robots can develop a symbolic structure with which they can communicate the names of a few objects with a remarkable degree of success. It is further shown that, although the referents may be interpreted differently on different occasions, the objects are usually named with only one form.

Over the past five years, the topic of the origins of language is gaining prominence as one of the big unresolved questions of cognitive science. Artificial Intelligence can make a major contribution to this problem by working out precise, testable models using grounded robotic agents which interact with a real world environment and communicate among themselves or with humans about this environment. A potential side effect op this basic research are new technologies for man-machine interaction based on the negotiation of shared conventions.

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Despite the relevance of much of Jakob von Uexkll's work to artificial intelligence and the cognitive sciences, it was largely ignored until the mid1980s. Since then, much research has been devoted to the study of embodied autonomous agents (robots) and artificial life. Such systems are typically said to `learn', `develop' and `evolve' in interaction with their environments. It could be argued that these self-organizing properties solve the problem of symbol or representation grounding in artificial intelligence research, and thus place autonomous agents in a position of semiotic interest. Here we discuss the relevance and implications of Jakob von Uexkll's theory of meaning to the study of artificial organisms and their use of representation and sign processes. Furthermore, we contrast his position with more mechanistic views, and examine the relation to recent theories of embodied cognition and its biological basis, in particular the work of Maturana and Varela. Finally, we address the issue of whether and to what extent artificial organisms are autonomous and capable of semiosis.

This paper presents a formal framework and outlines a method that autonomous agents can use to negotiate the semantics of their communication language at run-time. Such an ability is needed in open multi-agent systems so that agents can ensure they understand the implications of the utterances that are being made and so that they can tailor the meaning of the primitives to best fit their prevailing circumstances. To this end, the semantic space framework provides a systematic means of classifying the primitives along multiple relevant dimensions. This classification can then be used by the agents to structure their negotiation (or semantic fixing) process so that they converge to the mutually agreeable semantics that are necessary for coherent social interactions

Human language is unique among the communication systems of the natural world. The vocabulary of human language is unique in being both culturally-transmitted and symbolic. In this paper I present an investigation into the factors involved in the evolution of such vocabulary systems. I investigate both the cultural evolution of vocabulary systems and the biological evolution of learning rules for vocabulary acquisition.

This paper explores the hypothesis that pointing gesture recognition can be learned using a reward based system. An experiment with two four-legged robots is presented.

Advertising plays a key role in service oriented recommendation over a peer-to-peer network. The advertising problem can be considered as the problem of finding a common language to denote the peers' capabilities and needs. Up to now the current approaches to the problem of advertising revealed that the proposed solutions either a#ect the autonomy assumption or do not scale up the size of the network. We explain how an approach based on language games can be e#ective in dealing with the typical issue of advertising: do not require ex-ante agreement and to be responsive to the evolution of the network as an open system. In the

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.