While structured as an autobiography, this memoir exemplifies ways in which classic contributions to cybernetics (e.g., by Wiener, McCulloch & Pitts, and von Neumann) have fed into a diversity of current research areas, including the mathematical theory of systems and computation, artificial intelligence and robotics, computational neuroscience, linguistics, and cognitive science. The challenges of brain theory receive special emphasis. Action-oriented perception and schema theory complement neural network modeling in analyzing cerebral cortex, cerebellum, hippocampus, and basal ganglia. Comparative studies of frog, rat, monkey, ape and human not only deepen insights into the human brain but also ground an EvoDevoSocio view of “how the brain got language.” The rapprochement between neuroscience and architecture provides a recent challenge. The essay also assesses some of the social and theological implications of this broad perspective.

Key words: action-oriented perception, ape, architecture, artificial intelligence, automata theory, basal ganglia, brain theory, cerebellum, cerebral cortex, cognitive science, computational neuroscience, cybernetics, frog, hippocampus, human, language evolution, li

The article considers the complexities of thinking about the computer as a model of the mind. It examines the computer as being a model of the brain in several very different senses of “model‘. On the one hand the basic architecture of the first modern stored-program computers was „modeled on“ the brain by John von Neumann. Von Neumann also sought to build a mathematical model of the biological brain as a complex system. A similar but different approach to modeling the brain was taken by Alan Turing, who on the one hand believed that the mind simply was a universal computer, and who sought to show how brain-like networks could self-organize into Universal Turing Machines. And on the other hand, Turing saw the computer as the universal machine that could simulate any other machine, and thus any particular human skill and thereby could simulate human intelligence. This leads to a discussion of the nature of “simulation” and its relation to models and modeling. The article applies this analysis to a written correspondence between Ashby and Turing in which Turing urges Ashby to simulate his cybernetic Homeostat device on the ACE computer, rather than build a special machine.

This paper presents an architecture of autopoietic intelligent systems (AIS) as systems of automated “software production”-like processes based on meta-engineering (ME) theory. A self-producing AIS potentially displays the characteristics of artificial general intelligence (AGI). The architecture describes a meta-engineering system (MES) comprising many subsystems which serve to produce increasingly refined “software-production”-like processes rather than producing a solution for a specific domain. ME-theory involves a whole order of MES and the ME-paradox, expressing the fact that MES can potentially achieve a general problem-solving capability by means of maximal specialization. We argue that high-order MES are readily observable in software production systems (sophisticated software organizations) and that engineering practices conducted in such domains can provide a great deal of insight on how AIS can actually work.

Key words: autopoiesis, meta-engineering, meta-learning, software production system, artificial general intelligence.

Context: The discipline of knowledge management (KM) begins to understand a) that it should move towards a user-centred, socialized KM and b) which business objectives provide motivation to do so. However, it lacks ideas on how to reach the objective that it suggests and justifies. We contend in this paper that this change requires a more viable understanding of knowledge combined with a suitable model of social interaction, otherwise it will fail. Problem: The problem to be solved is to find a way to blend a model of social interaction and a suitable understanding of knowledge so that together they can contribute to the objective of implementing a “user-centred KM.” In this paper we show a solution articulated in several conceptual and experimental components and phases. Method: We use a systemic and cybernetic approach: systemic analysis of the problem, conception of a cybernetic approach, design of a systemic solution, and its evaluation in an experiment. The main methods used are systems engineering, cybernetic modelling, and knowledge engineering. Results: We propose seven interrelated results: 1. A defect analysis of KM; 2. The concept of knowledge as the “Logic of Experience”; 3. A set of five KM design principles; 4. The principle of “Knowledge Identity”; 5. The model of “Knowledge Cooperation”; 6. The architecture of a user-centred KM system; and 7. Insights from a KM experiment. Implications: Our results are useful for any stakeholder in today’s knowledge economy when they need to understand, design, build, nurture and support an organization’s capacity to learn and innovate for the benefit not only of the company’s financial owners but also of the individuals who work in it. Future research should urgently address the issues of “knowledge identity” and the “knowledge contract” and KM practice should design its next steps for moving towards a user-centred KM in conformity with the principle of “knowledge identity.” The paper links explicitly to radical constructivism and argues in favour of a radical constructivist foundation for KM in which knowledge is seen as the “Logic of Experience.” It also shows how this KM foundation can be extended with a social perspective and by that allow the individual and the social to be conceived of as complementary elements in one single KM system.

Key words: logic of experience, knowledge identity, knowledge cooperation, design for meaning, community of research, user-centred knowledge management

Biological regulation is what allows an organism to handle the effects of a perturbation, modulating its own constitutive dynamics in response to particular changes in internal and external conditions. With the central focus of analysis on the case of minimal living systems, we argue that regulation consists in a specific form of second-order control, exerted over the core (constitutive) regime of production and maintenance of the components that actually put together the organism. The main argument is that regulation requires a distinctive architecture of functional relationships, and specifically the action of a dedicated subsystem whose activity is dynamically decoupled from that of the constitutive regime. We distinguish between two major ways in which control mechanisms contribute to the maintenance of a biological organisation in response to internal and external perturbations: dynamic stability and regulation. Based on this distinction an explicit definition and a set of organisational requirements for regulation are provided, and thoroughly illustrated through the examples of bacterial chemotaxis and the lac-operon. The analysis enables us to mark out the differences between regulation and closely related concepts such as feedback, robustness and homeostasis.

Context: Luhmann’s theory of autopoietic social systems is increasingly receiving attention in the scholarly dispute about constructivism. Problem: The paper explores the transition from Kant’s “transcendental/empirical” to Luhmann’s “system/environment” distinction to provide a deepened understanding of Luhmann’s constructivist approach. Method: Luhmann’s construction of reality via the system/environment distinction is discussed with respect to preceding concepts provided by philosophical and system/cybernetic scholars such as Kant, Husserl, Piaget, von Glasersfeld, von Foerster, and Maturana & Varela. The innovativeness of Luhmann’s approach is then critically evaluated. Results: Luhmann’s contribution to constructivism is innovative only in the context of his stringent theory architecture of autopoietic meaning-based systems. Implications: The text is a contribution to the positioning of this approach as part of the philosophical and systems/cybernetics constructivist heritage.

Key words: Theory of social systems (TSS), observation, autopoiesis, self-reference & hetero-reference, meaning

W. Ross Ashby was a founder of both cybernetics and general systems theory. His systems theory outlined the operational structure of models and observers, while his cybernetics outlined the functional architecture of adaptive systems. His homeostat demonstrated how an adaptive control system, equipped with a sufficiently complex repertoire of possible alternative structures, could maintain stability in the face of highly varied and challenging environmental perturbations. The device illustrates his ‘law of requisite variety’, i.e. that a controller needs at least as many internal states as those in the system being controlled. The homeostat provided an early example of how an adaptive control system might be ill-defined vis – vis its designer, nevertheless solve complex problems. Ashby ran into insurmountable difficulties when he attempted to scale up the homeostat, and consequently never achieved the general purpose, brainlike devices that he had initially sought. Nonetheless, the homeostat continues to offer useful insights as to how the large analogue, adaptive networks in biological brains might achieve stability.

Excerpt: I claim that a mechanism that reconstructs and re-coordinates processes, rather than stores and retrieves labeled descriptions or procedures, is more consistent with what we know about human memory and perception (Clancey 1991, 1994). Such a process memory possibly cannot be built today, because we don’t know how to build the kind of self-organizing mechanism that is required (cf. Freeman 1991). But articulating how human cognition is different from a classical architecture helps delineate what aspects of situated robotic designs are still cast in the classical mold and remain to be freed of prevailing assumptions about the nature of memory and representations.

We propose six Information-Processing Principles (IPPs) that together describe a constructive, hierarchical system by which infants come to understand objects and events in the world around them. We then demonstrate the applicability of these principles to four specific domains of infant perception and/or cognition, (i.e., form perception, object unity, complex pattern perception, and understanding of causal events). In each case empirical developmental changes appear to be consistent with the IPPs. We then present the Constructivist Learning Architecture, a computational model of infant cognitive development. This model is based on the IPPs, and uses self-organizing, neurally based techniques from Kohonen (1997) and Hebb (1949). We then apply the model to the complex domain of infant understanding of causal events, and replicate many of the developmental changes found empirically. Finally, we discuss the applicability of this constructivist approach to infant cognitive development in general.

Key words: information-processing principles, constructivist learning architecture, infant cognition.

This paper presents a historical perspective on the origin of the enactive approach to cognitive science, starting chronologically from cybernetics, with the aim of clarifying its main concepts, such as enaction, autopoiesis, structural coupling and natural drift; thus showing their influences in computational approaches and models of cognitive architecture. Works of renowned authors, as well as some of their main commentators, were addressed to report the development of enactive approach. We indicate that the enactive approach transcends its original context within biology, and at a second moment within connectionism, changes the understanding of the relationships so far established between the body and the environment, and the ideas of conceptual relationships between the mind and the body. The influence on computational theories is of great importance, leading to new artificial intelligence systems as well as the proposition of complex, autopoietic and alive machines. Finally, the article stresses the importance of the enactive approach in the design of agents, understanding that previous approaches have very different cognitive architectures and that a prototypical model of enactive cognitive architecture is one of the largest challenges today.

Key words: cognitive scienceenactioncomplex systemscognitive architecture