The concept of autonomy is of crucial importance for understanding life and cognition. Whereas cellular and organismic autonomy is based in the self-production of the material infrastructure sustaining the existence of living beings as such, we are interested in how biological autonomy can be expanded into forms of autonomous agency, where autonomy as a form of organization is extended into the behaviour of an agent in interaction with its environment (and not its material self-production) In this thesis, we focus on the development of operational models of sensorimotor agency, exploring the construction of a domain of interactions creating a dynamical interface between agent and environment. We present two main contributions to the study of autonomous agency: First, we contribute to the development of a modelling route for testing, comparing and validating hypotheses about neurocognitive autonomy. Through the design and analysis of specific neurodynamical models embedded in robotic agents, we explore how an agent is constituted in a sensorimotor space as an autonomous entity able to adaptively sustain its own organization. Using two simulation models and different dynamical analysis and measurement of complex patterns in their behaviour, we are able to tackle some theoretical obstacles preventing the understanding of sensorimotor autonomy, and to generate new predictions about the nature of autonomous agency in the neurocognitive domain. Second, we explore the extension of sensorimotor forms of autonomy into the social realm. We analyse two cases from an experimental perspective: the constitution of a collective subject in a sensorimotor social interactive task, and the emergence of an autonomous social identity in a large-scale technologically-mediated social system. Through the analysis of coordination mechanisms and emergent complex patterns, we are able to gather experimental evidence indicating that in some cases social autonomy might emerge based on mechanisms of coordinated sensorimotor activity and interaction, constituting forms of collective autonomous agency.

In this paper two swarm intelligence algorithms are used, the first leading the “attention” of the swarm and the latter responsible for the tracing mechanism. The attention mechanism is coordinated by agents of Stochastic Diffusion Search where they selectively attend to areas of a digital canvas (with line drawings) which contains (sharper) corners. Once the swarm’s attention is drawn to the line of interest with a sharp corner, the corresponding line segment is fed into the tracing algorithm, Dispersive Flies Optimisation which “consumes” the input in order to generate a “swarmic sketch” of the input line. The sketching process is the result of the “flies” leaving traces of their movements on the digital canvas which are then revisited repeatedly in an attempt to re-sketch the traces they left. This cyclic process is then introduced in the context of autopoiesis, where the philosophical aspects of the autopoietic artist are discussed. The autopoetic artist is described in two modalities: gluttonous and contented. In the Gluttonous Autopoietic Artist mode, by iteratively focussing on areas-of-rich-complexity, as the decoding process of the input sketch unfolds, it leads to a less complex structure which ultimately results in an empty canvas; therein reifying the artwork’s “death”. In the Contented Autopoietic Artist mode, by refocussing the autopoietic artist’s reflections on “meaning” onto different constitutive elements, and modifying her reconstitution, different behaviours of autopoietic creativity can be induced and therefore, the autopoietic processes become less likely to fade away and more open-ended in their creative endeavour.

Key words: creativity, autopoiesis, stochastic diffusion search, dispersive flies optimisation, generative art.

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.

Key words: Agency, Autonomy, Downward Causation, Functionality, Intentionality, Meaning, Normativity, Self-organization, emergence

Dynamicism has provided cognitive science with important tools to understand some aspects of “how cognitive agents work” but the issue of “what makes something cognitive” has not been sufficiently addressed yet and, we argue, the former will never be complete without the latter. Behavioristic characterizations of cognitive properties are criticized in favor of an organizational approach focused on the internal dynamic relationships that constitute cognitive systems. A definition of cognition as adaptive-autonomy in the embodied and situated neurodynamic domain is provided: the compensatory regulation of a web of stability dependencies between sensorimotor structures is created and pre served during a historical/developmental process. We highlight the functional role of emotional embodiment: internal bioregulatory processes coupled to the formation and adaptive regulation of neurodynamic autonomy. Finally, we discuss a “minimally cognitive behavior program” in evolutionary simulation modeling suggesting that much is to be learned from a complementary “minimally cognitive organization program”

Key words: minimal cognition, adaptive autonomy, neurodynamic organization, cognition-as-it-could-be, emotional embodiment

The concept of agency is of crucial importance in cognitive science and artificial intelligence, and it is often used as an intuitive and rather uncontroversial term, in contrast to more abstract and theoretically heavy-weighted terms like “intentionality”, “rationality” or “mind”. However, most of the available definitions of agency are either too loose or unspecific to allow for a progressive scientific program. They implicitly and unproblematically assume the features that characterize agents, thus obscuring the full potential and challenge of modeling agency. We identify three conditions that a system must meet in order to be considered as a genuine agent: a) a system must define its own individuality, b) it must be the active source of activity in its environment (interactional asymmetry) and c) it must regulate this activity in relation to certain norms (normativity). We find that even minimal forms of proto-cellular systems can already provide a paradigmatic example of genuine agency. By abstracting away some specific details of minimal models of living agency we define the kind of organization that is capable to meet the required conditions for agency (which is not restricted to living organisms). On this basis, we define agency as an autonomous organization that adaptively regulates its coupling with its environment and contributes to sustaining itself as a consequence. We find that spatiality and temporality are the two fundamental domains in which agency spans at different scales. We conclude by giving an outlook to the road that lies ahead in the pursuit to understand, model and synthesize agents.

Upshot: University engineering qualifications are accredited according to various international accords that are perceived as a restrictive factor in curricula redesign. I argue that this may not be the case owing to the discipline-independent wide-ranging content that may populate the qualifications. Also, I point out that the educator’s teaching style is their choice and that educators may act as agents of change bringing ethical changes to their curricula. Finally, I question the role of the teacher and its future relevance in competition with artificial intelligence.

This paper describes a transdisciplinary theoretical-practical research, which address on the discussion about the possible applications of Multi-agent Systems, underlying the Maturana and Varela’s autopoietic concept considering the achievement of emergent results as heuristics to creativity. Autopoiesis (from the Greek “auto” which means “itself” and “poiesis” which means “creation”) describes the autonomous systems, able to self-reproduce and self-regulate, while iterating with the environment. In order to explore those concepts, we present Zer0, a game that invites the player to drift in a universe ruled by geometric shapes. Through interactions with other shapes, the player is able to evolve from a single line shape to more complex ones. Zer0 is a multi-agent system able to compose emergent music in real time. As interactions occur, chain reactions create the game soundtrack. There are two main agents involved: the player and the other shapes. While the player enjoys the ride, the other shapes are trying to interact with each other in order to expand their lifespan. The communication between agents is made through generated pulses, which are emitted by them and also serves as sonar, in order to perceive the environment.

Key words: Multi-agent systems, creativity, emergence, autopoiesis, artificial intelligence, interaction

Notions of embodiment, situatedness, and dynamics are increasingly being debated in cognitive sci ence. However, these debates are often carried out in the absence of concrete examples. In order to build intuition, this paper explores a model agent to illustrate how the perspective and tools of dynam ical systems theory can be applied to the analysis of situated, embodied agents capable of minimally cognitive behavior. Specifically, we study a model agent whose “nervous system” was evolved using a genetic algorithm to catch circular objects and to avoid diamond-shaped ones. After characterizing the performance, behavioral strategy and psychophysics of the best-evolved agent, its dynamics are analyzed in some detail at three different levels: (1) the entire coupled brain/body/environment sys tem; (2) the interaction between agent and environment that generates the observed coupled dynam ics; (3) the underlying neuronal properties responsible for the agent dynamics. This analysis offers both explanatory insight and testable predictions. The paper concludes with discussions of the overall picture that emerges from this analysis, the challenges this picture poses to traditional notions of rep resentation, and the utility of a research methodology involving the analysis of simpler idealized mod els of complete brain/body/environment systems.

Key words: dynamics, minimally-cognitive behavior, categorical perception

Maturana and Varela’s notion of autopoiesis has the potential to transform the conceptual foundation of biology as well as the cognitive, behavioral, and brain sciences. In order to fully realize this potential, however, the concept of autopoiesis and its many consequences require significant further theoretical and empirical development. A crucial step in this direction is the formulation and analysis of models of autopoietic systems. This article sketches the beginnings of such a project by examining a glider from Conway’s game of life in autopoietic terms. Such analyses can clarify some of the key ideas underlying autopoiesis and draw attention to some of the central open issues. This article also examines the relationship between an autopoietic perspective on cognition and recent work on dynamical approaches to the behavior and cognition of situated, embodied agents. Relevance: The article focuses on the theory of autopoiesis and related concepts such as structural coupling and cognitive domain.

The merits of Empirical Modelling (EM) principles and tools as a constructivist approach to computer science education are illustrated with reference to ways in which they have been used in teaching topics related to the standard computer science curriculum. The products of EM are interactive models – construals – that serve a sense-making role. Model-building proceeds in an incremental fashion through the construction of networks of definitions that reflect the observables, dependencies and agents associated with a current situation. The three principal case studies discussed (teaching bubblesort, solving Sudoku puzzles, and recognising groups from their abstract multiplication tables) highlight respects in which EM accounts for aspects of computing that cannot be effectively addressed by thinking primarily in terms of abstractions, procedures and mechanisms. The discussion of EM as a constructivist approach to computer science education is set in the context of an analysis of constructivism in computer science published by Ben-Ari in 2001. Reconciling EM’s constructivist epistemology with this analysis involves recognising its pretensions to a broader view of computer science.