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.

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.

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. Many believe that the major problem facing traditional artificial intelligence (and the functional theory of mind) is how to connect intelligence to the outside world. Some turned to robotic functionalism and a hybrid response, that attempts to rescue symbolic functionalism by grounding the symbol system with a connectionist hook to the world. Others turned to an alternative approach, embodied cognition, that emerged from an older tradition in biology, ethology, and behavioural modelling. Both approaches are contrasted here before a detailed exploration of embodiment is conducted. In particular we ask whether strong embodiment is possible for robotics, i.e. are robot "minds" similar to animal minds, or is the role of robotics to provide a tool for scientific exploration, a weak embodiment? We define two types of embodiment, Loebian and Uexkullian, that express two different views of the relation between body, mind and behaviour. It is argued that strong embodiment, either Loebian or ...

Summary. How can agents, natural or artificial, learn about the external environment based only on its internal state (such as the activation patterns in the brain)? There are two problems involved here: first, forming the internal state based on sensory data to reflect reality, and second, forming thoughts and desires based on these internal states. (Aristotle termed these passive and active intellect, respectively [1].) How are these to be accomplished? Chapters in this book consider mechanisms of the instinct for learning (chapter PERLOVSKY) and reinforcement learning (chapter IFTEKHARUDDIN; chapter WERBOS), which modify the mind’s representation for better fitting sensory data. Our approach (as those in chapters FREEMAN and KOZMA) emphasizes the importance of action in this process. Action plays a key role in recovering sensory stimulus properties that are represented by the internal state. Generating the right kind of action is essential to decoding the internal state. Action that maintains invariance in the internal state are important as it will have the same property as that of the represented sensory stimulus. However, such an approach alone does not address how it can be generalized to learn more complex

The concept of `social situatedness', i.e. the idea that the development of individual intelligence requires a social (and cultural) embedding, has recently received much attention in cognitive science and artificial intelligence research. The work of Lev Vygotsky who put forward this view already in the 1920s has influenced the discussion to some degree, but still remains far from well known. This paper therefore aims to give an overview of his cognitive development theory and discuss its relation to more recent work in primatology and socially situated artificial intelligence, in particular humanoid robotics.

Contemporary research in artificial environments has marked the need for autonomy in artificial agents. Autonomy has many interpretations in terms of the field within which it is being used and analyzed, but the majority of the researchers in artificial environments are arguing in favor of a strong and life-like notion of autonomy. Departing from this point the main aim of this paper is to examine the possibility of the emergence of autonomy in contemporary artificial agents. The theoretical findings of research in the areas of living and cognitive systems, suggests that the study of autonomous agents should adopt a systemic and emergent perspective for the analysis of the evolutionary development of the notions/properties of autonomy, functionality, intentionality and meaning, as the fundamental and characteristic properties of a natural agent. An analytic indication of the functional emergence of these concepts and properties is provided, based on the characteristics of the more general systemic framework of second-order cybernetic and of the interactivist framework. The notion of emergence is a key concept in such an analysis which in turn provides the ground for the theoretical evaluation of the autonomy of contemporary artificial agents with respect to the functional emergence of their capacities. The fundamental problems for the emergence of genuine autonomy in artificial agents are critically discussed and some design guidelines are provided.

An overview and selective history of the fundamental theory underlying traditional artificial intelligence, namely cognitivism, and an alternative to this approach are presented in this thesis. Major criticisms against cognitivism are explicated in detail, and the main principles of an alternative approach practiced in the last two decades, dubbed New AI, are presented. Among these principles are autonomy, situatedness and embodiment, which are then demonstrated in two embodied models. One of these models is an empirical one of the performance of hens in a visual discrimination task, and the other is a model of categorization, where both models use an exemplar-based categorization mechanism. The thesis concludes with an argument for studying the emergence of linguistic behavior and the acquisition and use of symbols, in order to build successful models of human-level intelligence. It is further argued that embodiment and situatedness are crucial in this endeavor.

> Context • The enactive paradigm in the cognitive sciences is establishing itself as a strong and comprehensive alternative to the computationalist mainstream. However, its own particular historical roots have so far been largely ignored in the historical analyses of the cognitive sciences.> Problem • In order to properly assess the enactive paradigm’s theoretical foundations in terms of their validity, novelty and potential future directions of development, it is essential for us to know more about the history of ideas that has led to the current state of affairs.> Method • The meaning of the disappearance of the field of cybernetics and the rise of second-order cybernetics is analyzed by taking a closer look at the work of representative figures for each of the phases – Rosenblueth, Wiener and Bigelow for the early wave of cybernetics, Ashby for its culmination, and von Foerster for the development of the second-order approach.> Results • It is argued that the disintegration of cybernetics eventually resulted in two distinct scientific traditions, one going from symbolic AI to modern cognitive science on the one hand, and the other leading from second-order cybernetics to the current enactive paradigm.> Implications • We can now understand that the extent to which the cognitive sciences have neglected their cybernetic parent is precisely the extent to which cybernetics had already carried the tendencies that would later find fuller expression in second-order cybernetics.> Key Words • W. Ross Ashby, Heinz von Foerster, constructivism, enactive cognitive science. 1.

© This paper is not for reproduction without permission of the author(s). Much research in cognitive science and AI has recently been devoted to the study of adaptive autonomous agents, such as robots and artificial life systems. Such agents are typically said to ‘learn, ‘develop ’ and ‘evolve ’ in close interaction with their environments. It could be argued that these self-organizing properties solve the problem of representation grounding in AI research, and thus also place such ‘artificial organisms ’ in a position of semiotic interest. This paper discusses the use of autonomous agents as models of sign processes and embodied enactive cognition. Furthermore, it addresses the question whether or to what extent such agents are themselves autonomous and capable of semiosis in that sense that living organisms are.