The ‘grounding problem ’ poses the question of how the function and internal mechanisms of a machine, natural or artificial, can be intrinsic to the machine itself, i.e. independent of an external designer or observer. Searle’s and Harnad’s analyses of the grounding problem are briefly reviewed as well as different approaches to solving it, based on the cognitivist and the enactive paradigms in cognitive science. It is argued that, although the two categories of grounding approaches differ in their nature and the problems they have to face, both, so far, fail to provide fully grounded systems for similar reasons: Only isolated parts of systems are grounded, whereas other, essential, parts are left ungrounded. Hence, it is further argued that grounding should instead be understood and approached as radical bottom-up development of complete robotic agents in interaction with their environment.

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, a network of the former type will be analyzed in the following. Figure 24 shows a characteristic trajectory of a successful robot controller of this type. As above, the robot starts off facing the upper left obstacle. It turns away from it to the left, enters the zone, and collects three objects on its first pass through the zone, turning slightly to the left towards each of them. As soon as it has left the zone it starts moving in a semi-circle to the left, which takes it back into the zone. In the zone it starts moving straight ahead again, takes a slight turn to the right to collect the upper object, and continues straight ahead out of the zone. The same pattern is repeated: as soon as it leaves the zone, it moves in a semi-circle to the left, which takes it back into the zone, where it starts moving straight forward again. Once more it performs a slight turn to the right to collect an object it would otherwise have missed. It continues to move straight ahead, leaves the zone, returns in another semi-circle, enters once more and moves straight ahead until the evaluation period ends.

Embodiment has become an important concept in many areas of cognitive science. There are, however, very different notions of exactly what embodiment is and what kind of body is required for what kind of embodied cognition. Hence, while many would agree that humans are embodied cognizers, there is much less agreement on what kind of artefact could be considered as embodied. This paper identifies and contrasts five different notions of embodiment which can roughly be characterized as (1) structural coupling between agent and environment, (2) historical embodiment as the result of a history of structural coupling, (3) physical embodiment, (4) `organismoid' embodiment, i.e. organism-like bodily form (e.g., humanoid robots), and (5) organismic embodiment of autopoietic, living systems. 1.