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The success of Artificial Life depends on whether it will help solving the conceptual problems of biology. Biology may be viewed as the science of the transformation of organizations. And, yet, biology lacks a theory of organization. We use this as an example of the challenge that Artificial Life must meet. "If - as I believe - physics and chemistry are conceptually inadequate as a theoretical framework for biology, it is because they lack the concept of function, and hence that of organization. [...] [P]erhaps, therefore, we should give the [...] computer scientists more of a say in the formulation of Theoretical Biology." -- Christopher Longuet-Higgins, 1969 [29] 1 Life and the organization problem in biology There are two readings of "life": "life" as an embodied phenomenon and "life" as a concept. Foucault [20] points out that up to the end of the eighteenth century life does not exist: only living beings. Living beings are but a class in the series of all things in the world. T...

Abstract — In this paper I sketch a rough taxonomy of self-organization which may be of relevance in the study of cognitive and biological systems. I frame the problem both in terms of the language Heinz von Foerster used to formulate much of second-order cybernetics as well as the language of current theories of self-organization and complexity. In particular, I defend the position that, on the one hand, selforganization alone is not rich enough for our intended simulations, and on the other, that genetic selection in biology and symbolic representation in cognitive science alone leave out the very important (self-organizing) characteristics of particular embodiments of evolving and learning systems. I propose the acceptance of the full concept of symbol with its syntactic, semantic, and pragmatic dimensions. I argue that the syntax should be treated operationally in second-order cybernetics.

Abstract — In this paper I sketch a rough taxonomy of self-organization which may be of relevance in the study of cognitive and biological systems. I frame the problem both in terms of the language of secondorder cybernetics as well as the language of current theories of self-organization and complexity. The goal of establishing such a taxonomy is to allow for a classification of different tools used both in Artificial Intelligence and Artificial Life, so that different aspects of cognitive and biological systems may be incorporated in more accurate models of such systems. In particular, I defend, on the one hand, that self-organization alone is not rich enough for our intended simulations, and on the other, that genetic selection in biology and symbolic representation in cognitive science alone leave out the very important (self-organizing) characteristics of particular embodiments of evolving and learning systems.

ABSTRACT. It is argued that the problems of emergence and the architecture of complexity can be solved by analysing the self-organizing evolution of complex systems. A generalized, distributed variation-selection model is proposed, in which internal and external aspects of selection and variation are contrasted. “Relational closure ” is introduced as an internal selection criterion. A possible application of the theory in the form of a pattern directed computer system for supporting complex problem-solving is sketched. 1 Emergence and self-organization Emergence is a classical concept in systems theory, where it denotes the principle that the global properties defining higher order systems or “wholes ” (e.g. boundaries, organization, control,...) can in general not

This work addresses the question: What are the basic design considerations for creating a synthetic model of the evolution of living systems (i.e. an `artificial life' system)? It can also be viewed as an attempt to elucidate the logical structure (in a very general sense) of biological evolution. However, with no adequate definition of life, the experimental portion of the work concentrates on more specific issues, and primarily on the issue of open-ended evolution. An artificial evolutionary system called Cosmos, which provides a virtual operating system capable of simulating the parallel processing and evolution of a population of several thousand self-reproducing computer programs, is introduced. Cosmos is related to Ray's established Tierra system [Ray 91], but there are a number of significant differences. A wide variety of experiments with Cosmos, which were designed to investigate its evolutionary dynamics, are reported. An analysis of the results is presented, with particular attention given to the role of contingency

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.

Introduction Computer technology has only recently become advanced enough to solve the problems it creates with its own interface. One solution, virtual reality (VR), immediately raises fundamental issues in both semantics and epistemology. Broadly, virtual reality is that aspect of reality which people construct from information, a reality which is potentially orthogonal to the reality of mass. Within computer science, VR refers to interaction with computer generated spatial environments, environments constructed to include and immerse those who enter them. VR affords non-symbolic experience within a symbolic environment. Since people evolve in a spatial environment, our knowledge skills are anchored to interactions within spatial environments. VR design techniques, such as scientific visualization, map digital information onto spatial concepts. When our senses are immersed in stimuli from the virtual world, our minds construct a closure to crea

A proposal for the biological grounding of intrinsic teleology and sense-making through the theory of autopoiesis is critically evaluated. Autopoiesis provides a systemic language for speaking about intrinsic teleology but its original formulation needs to be elaborated further in order to explain sense-making. This is done by introducing adaptivity, a many-layered property that allows organisms to regulate themselves with respect to their conditions of viability. Adaptivity leads to more articulated concepts of behaviour, agency, sense-construction, health, and temporality than those given so far by autopoiesis and enaction. These and other implications for understanding the organismic generation of values are explored. 1.

What is the enactive approach to cognition? Over the last 15 years this banner has grown to become a respectable alternative to traditional frameworks in cognitive science. It is at the same time a label with different interpretations and upon which different doubts have been cast. This paper elaborates on the core ideas that define the enactive approach and their implications: autonomy, sensemaking, emergence, embodiment, and experience. These are coherent, radical and very powerful concepts that establish clear methodological guidelines for research. The paper also looks at the problems that arise from taking these ideas seriously. The enactive approach has plenty of room for elaboration in many different areas and many challenges to respond to. In particular, we concentrate on the problems surrounding several theories of value-appraisal and valuegeneration. The enactive view takes the task of understanding meaning and value very seriously and elaborates a proper scientific alternative to reductionist attempts to tackle these issues by functional localization. Another area where the enactive framework can make a significant contribution is social interaction and