title: How (not) to model autonomous behaviour.
abstract: Autonomous systems are the result of self-sustaining processes of constitution of an identity under precarious circumstances. They may transit through different modes of dynamical engagement with their environment, from committed ongoing coping to open susceptibility to external demands. This paper discusses these two statements and presents examples of models of autonomous behaviour using methods in evolutionary robotics. A model of an agent capable of issuing self-instructions demonstrates the fragility of modelling autonomy as a function rather than as a property of a system's organization. An alternative model of behavioural preference based on homeostatic adaptation avoids this problem by establishing a mutual constraining between lower-level processes (neural dynamics and sensorimotor interaction) and higher-level metadynamics (experience-dependent, homeostatic triggering of local plasticity and re-organization). The results of these models are lessons about how strong autonomy should be approached: neither as a function, nor as a matter of external vs. internal determination.
authors: Centre for Computational Neuroscience and Robotics, Centre for Research in Cognitive Science (COGS), University of Sussex, Brighton BN1 9QH, UK. ezequiel@sussex.ac.uk
authors: Ezequiel A
authors: EA
authors: Di Paolo
authors: Hiroyuki
authors: H
authors: Iizuka
mesh_terms: D000818:Animals; D001519:Behavior / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D006801:Humans; D033182:Intention; D019369:Life; D008954:Models, Biological; D026684:Personal Autonomy; D014836:Volition / Q000502:physiology*