Kravchenko (2007) suggests replacing Turing’s suggestion for explaining cognizers’ cognitive capacity through autonomous robotic modelling by ‘autopoiesis’, Maturana’s extremely vague metaphor for the relations and interactions among organisms, environments, and various subordinate and superordinate systems (‘autopoietic systems’) therein. I suggest that this would be an exercise in hermeneutics rather than causal explanation.

Key words: autonomy, distributed cognition, robotic capacity, symbol grounding, turing test.

This paper examines Aristotle’s notion of autonomy and its implication for the mechanicism/autonomy debate. We introduce the basic principles of Aristotle’s scientific framework, including his theory of four causes for the explanation of nature. We draw parallels between these notions of autonomy and causation and autopoietic theory, dynamical systems and robotics, suggesting that they may be compatible with Aristotle’s framework. We argue that understanding the problem of design of autonomous robots may benefit from the consideration of integration of Aristotle’s causes, while robotics, in turn, may contribute to the debate providing a common ground for epistemological and ontological notions of autonomy.

Key words: autonomy, aristotle, causation, autopoiesis, embodied cognition, mechanicism, robotics.

The internalist (computational) account of cognition is questioned and the explanatory power of the biology of cognition in resolving epistemological issues is emphasized. It is argued that, far from being an autonomous activity within the brains of cognizers which generates input/output capacity and can be auditioned by the Turing Test, cognition is a function of living systems as unities of interactions that exist in an environment in structural coupling. Therefore, it is distributed.

Key words: autonomy, behavior, distributed cognition, environment, structural coupling.

Although work on computational and robotic modelling of cognition is highly diverse, as an empirical method it can be roughly divided into at least two clearly different, though non-exclusive branches, motivated to evaluate the sufficiency or the necessity of theories when it comes to accounting for data and/or other observations. With the rising profile of theories of situated/embodied cognition, a third non-exclusive avenue for investigation has also gained in popularity, the investigation of agent-environment embedding or more generally, exploration. Still in its infancy, and often confused with sufficiency testing, this relatively new kind of modelling, which is theoryrather than data-driven, investigates the role of the environment in shaping the ontogenetic and/or phylogenetic development of situated agency. Each of these three approaches presents many issues that modellers must be sensitive to, both in the design of experiments, and in the conclusions that can be drawn from them. This paper highlights some of these issues, provides examples, and addresses the contribution of computational/robotic modelling to cognitive science, as well as some of its limitations.

Key words: cognitive modelling, cognitive robotics, embodied cognition, scientific method

We argue that the concepts of mechanism and autonomy appear to be antagonistic when autonomy is conflated with agency. Once these concepts are disentangled, it becomes clearer how autonomy could emerge from complex forms of control – especially, homeostatic regulatory systems. While research in AI and robotics would do well to continue incorporating biomimetic strategies, we propose that invoking models of allostatic mechanisms is a better way to understand how autonomy in artificial systems can be enhanced.