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Is it possible to guide the process of self-organisation towards specific patterns and outcomes? Wouldn’t this be self-contradictory? After all, a self-organising process assumes a transition into a more organised form, or towards a more structured functionality, in the absence of centralised control. Then how can we place the guiding elements so that they do not override rich choices potentially discoverable by an uncontrolled process?

This book presents different approaches to resolving this paradox. In doing so, the presented studies address a broad range of phenomena, ranging from autopoietic systems to morphological computation, and from small-world networks to information cascades in swarms. A large variety of methods is employed, from spontaneous symmetry breaking to information dynamics to evolutionary algorithms, creating a rich spectrum reflecting this emerging field.

Demonstrating several foundational theories and frameworks, as well as innovative practical implementations, Guided Self-Organisation: Inception, will be an invaluable tool for advanced students and researchers in a multiplicity of fields across computer science, physics and biology, including information theory, robotics, dynamical systems, graph theory, artificial life, multi-agent systems, theory of computation and machine learning.





On the Cross-Disciplinary Nature of Guided Self-Organisation

Self-organisation is pervasive: neuronal ensembles self-organise into complex spatio-temporal spike patterns which facilitate synaptic plasticity and long-term consolidation of information; large-scale natural or social systems, as diverse as forest fires, landslides, or epidemics, produce spontaneous scale-invariant behaviour; robotic modules self-organise into coordinated motion patterns; individuals within a swarm achieve collective coherence out of isolated actions; and so on. Selforganisation is also valuable: the resultant increase in an internal organisation brings benefits to the (collective) organism, be it a learning brain, a co-evolving ecosystem, an adapting modular robot, or a re-configuring swarm. These benefits are typically realised in increased resilience to external disturbances, adaptivity to novel tasks, and scalability with respect to new challenges. However, self-organisation is difficult to engineer on demand: the intricate fabric of interactions within a self-organising system cannot follow a simple-minded blueprint and resists crude interventions.
Mikhail Prokopenko, Daniel Polani, Nihat Ay

Foundational Frameworks


Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis

In recent decades, the scientific study of complex systems (Bar-Yam 1997; Mitchell 2009) has demanded a paradigm shift in our worldviews (Gershenson et al. 2007; Heylighen et al. 2007). Traditionally, science has been reductionistic. Still, complexity occurs when components are difficult to separate, due to relevant interactions. These interactions are relevant because they generate novel informationwhich determines the future of systems. This fact has several implications (Gershenson 2013).
Nelson Fernández, Carlos Maldonado, Carlos Gershenson

Generating Functionals for Guided Self-Organization

One may take it as a running joke, that complex systems are complex since they are complex. It is however important to realize, this being said, that complex systems come in a large varieties, and in many complexity classes, ranging from relatively simple to extraordinary complex. One may distinguish in this context between classical and modern complex system theory. In the classical approach one would typically study a standardized model, like the Lorentz model or the logistic map, being described usually by maximally a handful of variables and parameters (Gros 2008). Many real-world systems are however characterized by a very large number of variables and control parameters, especially when it comes to biological and cognitive systems. It has been noted, in this context, that scientific progress may generically be dealing with complexity barriers of various severities, in far reaching areas like medicine and meteorology (Gros 2012b), when researching real-world natural or biological complex systems.
Claudius Gros

Empowerment–An Introduction

Is it better for you to own a corkscrew or not? If asked, you as a human being would likely say “yes”, but more importantly, you are somehow able to make this decision. You are able to decide this, even if your current acute problems or task do not include opening a wine bottle. Similarly, it is also unlikely that you evaluated several possible trajectories your life could take and looked at them with and without a corkscrew, and then measured your survival or reproductive fitness in each. When you, as a human cognitive agent, made this decision, you were likely relying on a behavioural “proxy”, an internal motivation that abstracts the problem of evaluating a decision impact on your overall life, but evaluating it in regard to some simple fitness function. One example would be the idea of curiosity, urging you to act so that your experience new sensations and learn about the environment. On average, this should lead to better and richer models of the world, which give you a better chance of reaching your ultimate goals of survival and reproduction.
Christoph Salge, Cornelius Glackin, Daniel Polani

A Framework for the Local Information Dynamics of Distributed Computation in Complex Systems

The nature of distributed computation has long been a topic of interest in complex systems science, physics, artificial life and bioinformatics. In particular, emergent complex behavior has often been described from the perspective of computation within the system (Mitchell 1998b,a) and has been postulated to be associated with the capability to support universal computation (Langton 1990; Wolfram 1984c; Casti 1991).
Joseph T. Lizier, Mikhail Prokopenko, Albert Y. Zomaya

Quantifying Synergistic Mutual Information

Synergy is a fundamental concept in complex systems that has received much attention in computational biology (Narayanan et al. 2005; Balduzzi and Tononi 2008). Several papers (Schneidman et al. 2003a; Bell 2003; Nirenberg et al. 2001;Williams and Beer 2010) have proposed measures for quantifying synergy, but there remains no consensus which measure is most valid.
Virgil Griffith, Christof Koch

Coordinated Behaviour and Learning within an Embodied Agent


On the Role of Embodiment for Self-Organizing Robots: Behavior As Broken Symmetry

Embodiment and SO form two cornerstones of both modern robotics and the understanding of human and animal intelligence. In particular, the role of the embodiment for the behavior of both artificial and natural beings has become of much and increasing interest in recent times. In robotics, there are essentially two attitudes towards the physical embodiment. On the one hand, with rule based systems and/or systems intended to execute a given motion plan, embodiment is more or less considered as a (nasty) problem opposing the execution of the plan. On the other hand, it is well believed and verified by many examples that living beings are taking much advantage from the physico-mechanical properties of their bodies in order to create natural motion patterns.
Ralf Der

Robot Learning by Guided Self-Organization

Self-organizing processes are not only crucial for the development of living beings, but can also spur new developments in robotics, e. g. to increase fault tolerance and enhance flexibility, provided that the prescribed goals can be realized at the same time. This combination of an externally specified objective and autonomous exploratory behavior is very interesting for practical applications of robot learning. In this chapter, we will present several forms of guided self-organization in robots based on homeokinesis.
Georg Martius, Ralf Der, J. Michael Herrmann

On the Causal Structure of the Sensorimotor Loop

In recent years, the application of information theory to the field of embodied intelligence has turned out to be extremely fruitful. Here, several measures of information flow through the sensorimotor loop of an agent are of particular interest. There are mainly two ways to apply information theory to the sensorimotor setting.
Nihat Ay, Keyan Zahedi

Action Switching in Brain-Body-Environment Systems

In recent years, the cognitive sciences have been converging upon an integrated perspective, a perspective that reframes behavior and cognition as a special type of self-organization that arises through the nonlinear, distributed interactions between brain, body and environment (abbreviated BBE). The BBE perspective has been separately developed by multiple lines of research such as the extended mind (Clark and Chalmers 1998), distributed cognition (Hutchins 2000), embodied cognition (Clark 1998), enactive cognition (Noė 2005; Thompson 2007; Varela et al. 1992) ), situated cognition (Clancey 1997; Hutchins 1995), and the dynamical approaches to cognition (Beer 1995b; Thelen and Smith 1996; Kelso 1995; Port and van Gelder 1995). These different theories all emphasize different elements of the BBE; either the body, or the environment, or the temporal element. But their different theories are friendly to each other and can be brought together into a broader, integrated perspective. By bringing focus to all of the relevant components and their interactions, cognitive systems are transformed into seemingly self-organizing systems, in which behavior and cognition become a dynamical process that unfolds through distributed interactions (Kelso 1995; Maturana and Varela 1980; Thompson 2007).
Eran Agmon

Guided Self-Organization of Input-Driven Recurrent Neural Networks

To understand the world around us, our brains solve a variety of tasks. One of the crucial functions of a brain is to make predictions of what will happen next, or in the near future. This ability helps us to anticipate upcoming events and plan our reactions to them in advance. To make these predictions, past information needs to be stored, transformed or used otherwise. How exactly the brain achieves this information processing is far from clear and under heavy investigation. To guide this extraordinary research effort, neuroscientists increasingly look for theoretical frameworks that could help explain the data recorded from the brain, and to make the enormous task more manageable. This is evident, for instance, through the funding of the billion-dollar ”Human Brain Project”, of the European Union, amongst others. Mathematical techniques from graph and information theory, control theory, dynamical and complex systems (Sporns 2011), statistical mechanics (Rolls and Deco 2010), as well as machine learning and computer vision (Seung 2012; Hawkins and Blakeslee 2004), have provided new insights into brain structure and possible function, and continue to generate new hypotheses for future research.
Oliver Obst, Joschka Boedecker

Swarms and Networks of Agents


Measuring Information Dynamics in Swarms

We propose a novel, information theoretic characterization of dynamics within swarms, through explicitly measuring the extent of collective communications and tracing collectivememory. These elements of distributed computation provide complementary views into the capacity for swarm coherence and reorganization. The approach deals with both global and local information dynamics ultimately discovering diverse ways in which an individual’s location within the group is related to its information processing role.
Jennifer M. Miller, X. Rosalind Wang, Joseph T. Lizier, Mikhail Prokopenko, Louis F. Rossi

Guiding Designs of Self-Organizing Swarms: Interactive and Automated Approaches

Engineering design has traditionally been a top-down process in which a designer shapes, arranges and combines various components in a specific, precise, hierarchical manner, to create an artifact that will behave deterministically in an intended way (Minai et al. 2006; Pahl et al. 2007). However, this process does not apply to complex systems that show self-organization, adaptation and emergence. Complex systems consist of a massive amount of simpler components that are coupled locally and loosely, whose behaviors at macroscopic scales emerge partially stochastically in a bottom-up way. Such emergent properties of complex systems are often very robust and dynamically adaptive to the surrounding environment, indicating that complex systems bear great potential for engineering applications (Ottino 2004).
Hiroki Sayama

Mutual Information As a Task-Independent Utility Function for Evolutionary Robotics

The design of the control system for a swarm of robots is not a trivial enterprise. Above all, it is difficult to define which are the individual rules that produce a desired swarm behaviour without an a priori knowledge of the system features. For this reason, evolutionary or learning processes have been widely used to automatically synthesise group behaviours (see, for instance, Matarić 1997; Quinn et al. 2003; Baldassarre et al. 2007). In this paper, we investigate the use of information-theoretic concepts such as entropy and mutual information as task-independent utility functions for mobile robots, which adapt on the basis of an evolutionary or learning process. We believe that the use of implicit and general purpose utility functions—fitness functions or reward/error measures—can allow evolution or learning to explore the search space more freely, without being constrained by an explicit description of the desired solution. In this way, it is possible to discover behavioural and cognitive skills that play useful functionalities, and that might be hard to identify beforehand by the experimenter without an a priori knowledge of the system under study. Such task-independent utility functions can be conceived as universal intrinsic drives toward the development of useful behaviours in adaptive embodied agents.
Valerio Sperati, Vito Trianni, Stefano Nolfi

Evolution of Complexity and Neural Topologies

One of the grandest and most intriguing self-organizing systems is nature itself. Whether couched in terms of evolutionary theory (Darwin 1859), information theory (Avery 2003), or thermodynamics and maximum physical entropy (Jaynes 1957a,b; Swenson 1989) natural processes have yielded a remarkable diversity of behavioral and organizational levels of complexity ranging from microbes to man.
Larry S. Yaeger

Clustering and Modularity in Self-Organized Networks

Many biological, artificial, and social systems are self-organized. Though an overarching, exhaustive definition of self-organization is elusive, there is general agreement on many of the properties that self-organized systems can be characterized by: they are global systems, composed of many, usually identical, micro level components. These components interact locally, while the system shows emergence of global dynamics not directly observable, measurable, quantified, or defined at the local level (Prokopenko 2009).
Somwrita Sarkar, Peter A. Robinson


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