Proceedings of the Workshop Autopoiesis and Perception held in Dublin City University, August 25th & 26th 1992 edited by Barry McMullin Autopoiesis and a Biology of Intentionality Francisco J. Varela As everybody here knows, autopoiesis is a neologism, introduced in 1971 by H. Maturana and myself to designate the organization of a minimal living system. The term became emblematic of a view of the relation between an organism and its medium, where its self constituting and autonomous aspects are put at the center of the stage. From 1971, until now much has happened to reinforce this perspective. Some of the developments have to do with the notion of autopoiesis itself in relation to the cellular organization and the origin of life. Much more has to do with the autonomy and self-organizing qualities of the organism in relation with its cognitive activity. Thus in contrast to the dominant cognitivist, symbol-processing views of the 70's today we witness in cognitive science a renaissance of the concern for the embeddedness of the cognitive agent, natural or artificial. This comes up in various labels as nouvelle-AI (Brooks 1991c), the symbol grounding problem (Harnad 1991), autonomous agents in artificial life (Varela & Bourgine 1992), or situated functionality (Agree 1988), to cite just a few self- explanatory labels used recently. Any of these developments could merit a full talk; obviously I cannot do that here. My intention rather, profiting from the position of opening this gathering, is to try to indicate some fundamental or foundational issues of the relation between autopoiesis and perception. Whence the title of my talk: a biology of intentionality. Since the crisis of classical cognitive science has thrown open the issue of intentionality, in my eyes autopoiesis provides a natural entry into a view of intentionalty that is seminal in answering the major obstacles that have been addressed recently. I'll came back to that at the end. Let me begin at the beginning. The Causal and Symbolic Explanatory Duality as a Framework for Understanding Vision Noel Murphy The conventional approach to interpreting biological vision systems and experimenting with computer vision systems has been overwhelmingly dominated by a representational view of information. Even more recent connectionist approaches, though embodying a substantial change in viewpoint, have only involved a change of the *type* of representation, to one of a distributed nature. An alternative view is the notion of information as being constructed and co-dependent rather than instructional and referential. This is an interpretation based on the more embracing viewpoint of the complementary causal descriptions and symbolic descriptions playing clearly defined interrelated and dual roles, rather than mutually exclusive, or even muddled roles. This paper examines this radical change in perspective and compares it with a causality framework and with a position on the nature of perception which is based on the idea of universals. Relativistic Ontologies, Self-Organization, Autopoiesis, and Artificial Life: A Progression in the Science of the Autonomous Part I -- The Philosophical Foundations David Vernon & Dermot Furlong Autopoiesis is a very powerful way of looking at and dealing with autonomous systems. It also has some major implications for the philosophy of science. Unfortunately, it is not clear in what philosophical context one should go about using autopoiesis. In this paper, we look at these issues, touching upon the inadequacies of conventional (positivistic) ontologies and philosophies of science, and we briefly describe an alternative relativistic ontology. We argue that self-organization is a necessary condition for autonomous systems and we highlight the difficulties that this raises for conventional representational approaches to autonomous systems. We discuss a methodology for discourse in relativistic ontology (Systematics) and, based on this, we argue in favour of a spectrum of autonomy. In a sister as a particular instance of autonomy in this spectrum. We proceed to describe the progress which has been made towards the development of a computational simulation of autopoietic organization, beginning with a formulation in terms of the calculus of indications (incorporating Varela's extensions to include autonomous forms), and incorporating the Systematic formulation. Relativistic Ontologies, Self-Organization, Autopoiesis, and Artificial Life: A Progression in the Science of the Autonomous Part II -- A Scientific Development David Vernon & Dermot Furlong In a sister paper, we have looked at the philosophical aspects of the development of autonomous systems, touching upon the inadequacies of conventional (positivistic) ontologies and philosophies of science, and we have described an alternative relativistic ontology. We argued that self-organization is a necessary condition for autonomous systems and we highlighted the difficulties that this raises for conventional representational approaches to autonomous systems. We discussed a methodology for discourse in relativistic ontology (Systematics) and, based on this, we argued in favour of a spectrum of autonomy. In this paper, we try to show how autopoiesis can be interpreted as a particular instance of autonomy in this spectrum. We now proceed to describe the progress which has been made towards the development of a computational simulation of autopoietic organization, beginning with a formulation in terms of the Calculus of Indications (incorporating Varela's extensions to include autonomous forms), and incorporating the Systematic formulation. Perception, Adaptation and Learning Alvaro Moreno, Juan Julian Merelo & Arantza Etxeberria We attempt to distinguish, in a biological frame, ontogenetical adaptation from learning. Ontogenetical adaptation arises as a second order (sensorimotor) loop on the ground of the operational closure that provides autonomy and reproductive identity to the living system. Adaptation ensures, through perception, the functional correlation between metabolic-motor states and the states of the environment. Learning brings about a qualitative change in regard to adaptation, the most generic and simple form of optimization at an individual scale. It implies the idea of new knowledge, in the sense that the organism links what formerly appeared as an undistinguished whole. In other words, it means the capability to change its own codes of meaning. Finally, we outline some basic ideas for modelling an adaptive sensor embedded in a (partially) autonomous system, which implies the former distinction between adaptation and learning. Artificial Darwinism: The Very Idea! Barry McMullin The realisation of artificial Darwinian evolution is one conceivable -- indeed, more or less obvious -- route toward the realisation of a growth of knowledge (or “complexity”) in artificial systems. This paper explores the current state of the art in achieving Artificial Darwinism, and the prospects for further progress. In particular, I reassess the seminal work of von Neumann on evolution in cellular automata von Neumann 1951; 1966a; 1966b). I also review the *Genetic Algorithm* also review the Genetic Algorithm (Holland 1975), and the VENUS (Rasmussen et al. 1990) and Tierra (Ray 1992) systems. I attempt to relate this to the work of Varela, and others, on the realisation of *autopoiesis* in related (discrete, 2-dimensional, homogenous) spaces (Varela et al. 1974; Zeleny 1977; Zelany & Pierre 1976), and I also revisit the Holland alpha-universes (Holland 1976; McMullin 1992d). I suggest that while both open-ended heredity (von Neumann style “self-reproduction”) and spontaneous autopoiesis have been separately demonstrated in such systems, the combination of the two remains a difficult outstanding problem. I conclude by outlining an avenue for further investigation. Reality Paradigms, Perception, and Natural Science. The Relevance of Autopoiesis Dermot Furlong & amp; David Vernon There is an ancient philosophical principle which states that Being is prior to Knowledge. That statement, I would suggest, holds little interest for the majority of scientists and technologists, who quite likely would not see the relevance of the remark to their research activities or, indeed, to their lives. Such is the entrenchment in our world view, our perceptual reality-paradigm, that we do not, and indeed almost cannot, recognise that how we see `the world' is dependent on what we are, i.e. on our ontological status. To put it bluntly, seeing the world, for normal science, is the construction of a represention of an external reality by the scientific spectator, which representation must be probed to determine its hidden, primary, mathematically-describable, and fundamentally mechanistic, basis. Such a scientific `spectator consciousness', and the scientific methodology associated with it, is the product of a cultural development which, with its roots in antiquity, found most complete reinforcement in the successes of the mathematical physics of nineteenth century classical science. For the greater part this spectator science remains remarkably unscathed despite the various undermining developments of modern, twentieth century, physics, and is now finding concrete expression in the relatively new discipline of cognitive science, as we assail the question of consciousness -- `just about the last surviving mystery', to quote Daniel Dennett. Along the way, the terrain underfoot of the `secure stride of science' has been substituted without any enquiry as to its suitability, moving from the physical, to the biological, to the mental, in the quest for absolute certainty, necessity, and completeness, or at least an acceptable approximation to same! However, what is suggested here is that normal science is fundamentally flawed when it is applied to the domains of Life and Mind, and that, furthermore, very much involved in that flaw is our non-recognition of the adopted perceptual reality-paradigm. That is to say, our mis-conception of science has very much to do with our mis-conception of perception. And what is wrong with our conception of science in its application to Life and Mind is that the analytic reductionism which characterises the spectator consciousness stance can never capture the organisational distinctions which characterise living or cognizing beings. Scalpels and microscopes may be useful, but not for the discovery of Life or Mind, for when the analysis is done, that which is essential is gone. Constructivist Artificial Life, and Beyond Alexander Riegler Within this paper I provide an epistemological context for Artificial Life projects. Later on, the insights which such projects will exhibit may be used as a general direction for further Artificial Life implementations. The purpose of such a model is to demonstrate by way of simulation how higher cognitive structures may emerge from building invariants by simple sensorimotor beings. By using the bottom-up methodology of Artificial Life, it is hoped to overcome problems that arise from dealing with complex systems, such as the phenomenon of cognition. The research will lead to both epistemological and technical implications. The proposed ALife model is intended to point out the usefulness of an interdisciplinary approach including methodological approaches from disciplines such as Artificial Intelligence, Cognitive Science, Theoretical Biology, and Artificial Life. I try to put them in one single context. The epistemological background which is necessary for this purpose comes from the ideas developed in both epistemological and psychological Constructivism. The model differs from other ALife approaches -- and is somewhat radical in this sense -- as it tries to start on the lowest possible level, i.e. avoids several a priori assumptions and anthropocentric ascriptions. Due to this characterization, the project may be alternatively viewed as testing the complementary relationship between epistemology and methodology. Reconstructing AI Conor Doherty Symbolic AI is argued to be epistemologically and ontologically necessary but insufficient for constructing robust AI. Two principles, embodiment and situatedness, are elaborated which any global theory of AI must incorporate. These principles require autonomous robotics to form a basis for AI. Learning is the key to the development of more autonomous robots. Artificial neural networks are evaluated for their ability to learn to integrate robust sensory categorisation with motor control. The future relationship of artificial neural networks to symbolic AI is speculated on.