Guided Self-Organisation (GSO) aims to leverage the strengths of self-organisation while still being able to direct the outcome of the self-organising process. GSO approaches typically use a combination of task-independent objectives and task-dependent constraints whose interplay gives rise to an increase in organisation, such as structure or functionality of a system, while avoiding explicit guidance of every local interaction between system elements. <br /> <br />In order to understand how to best deploy the GSO methodology for different problems and in a variety of systems, a sufficiently general mathematical framework needs to be devised, covering multiple scales and contexts. While progress has been made towards this goal by formalising aspects of GSO within information theory, thermodynamics and dynamical systems, it is important for the field to further identify common principles of guidance across different systems, and integrate them in a unified theory of GSO. General methods for characterising GSO systems in a principled way, with the view of ultimately allowing them to be guided toward pre-specified goals, are of particular interest in this respect. <br /> <br />The goal of this Research Topic in the Computational Intelligence specialty section of Frontiers in Robotics and AI is to collect current research on theoretical aspects of GSO addressing the points above, as well as to present applications of GSO in a variety of systems. This may include topics presented at the seventh international workshop on Guided Self-Organisation (GSO-2014), such as guidance of the self-organisation of behaviours for embodied robots, guiding the self-organised activity in real and artificial neuronal networks, understanding the driving forces for the emergence of structure-function relations in complex biological networks, elucidating collective information processing in animal swarms, exploring the connection between information-theoretic quantities and the limits of computation, as well as guiding self-organisation for morphogenetic purposes. However, we also welcome submissions beyond these topics from key areas of interest for GSO, such as information-driven self-organisation, complexity measures, adaptive behaviour, machine learning, distributed computation, computational neuroscience, and cooperative and modular robotics.
Guided Self-Organisation (GSO) aims to leverage the strengths of self-organisation while still being able to direct the outcome of the self-organising process. GSO approaches typically use a combination of task-independent objectives and task-dependent constraints whose interplay gives rise to an increase in organisation, such as structure or functionality of a system, while avoiding explicit guidance of every local interaction between system elements. <br /> <br />In order to understand how to best deploy the GSO methodology for different problems and in a variety of systems, a sufficiently general mathematical framework needs to be devised, covering multiple scales and contexts. While progress has been made towards this goal by formalising aspects of GSO within information theory, thermodynamics and dynamical systems, it is important for the field to further identify common principles of guidance across different systems, and integrate them in a unified theory of GSO. General methods for characterising GSO systems in a principled way, with the view of ultimately allowing them to be guided toward pre-specified goals, are of particular interest in this respect. <br /> <br />The goal of this Research Topic in the Computational Intelligence specialty section of Frontiers in Robotics and AI is to collect current research on theoretical aspects of GSO addressing the points above, as well as to present applications of GSO in a variety of systems. This may include topics presented at the seventh international workshop on Guided Self-Organisation (GSO-2014), such as guidance of the self-organisation of behaviours for embodied robots, guiding the self-organised activity in real and artificial neuronal networks, understanding the driving forces for the emergence of structure-function relations in complex biological networks, elucidating collective information processing in animal swarms, exploring the connection between information-theoretic quantities and the limits of computation, as well as guiding self-organisation for morphogenetic purposes. However, we also welcome submissions beyond these topics from key areas of interest for GSO, such as information-driven self-organisation, complexity measures, adaptive behaviour, machine learning, distributed computation, computational neuroscience, and cooperative and modular robotics.