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Special Issue "Entropy Methods in Guided Self-Organization"

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A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: closed (31 January 2014)

Special Issue Editors

Guest Editor
Dr. Mikhail Prokopenko (Website)

CSIRO ICT Centre, Adaptive Systems, PO Box 76, Epping, NSW 1710, Australia
Interests: guided self-organization; information theory; machine learning; complex networks
Guest Editor
Dr. Carlos Gershenson (Website)

Head of Computer Science Department, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, A.P. 20-726, 01000, México, D.F., Mexico
Interests: self-organizing systems, complexity, information, artificial life, philosophy

Special Issue Information

Dear Colleague,

The goal of Guided Self-Organization (GSO) is to leverage the strengths of self-organization while still being able to direct the outcome of the self-organizing process. GSO typically has the following features: (i) an increase in organization (structure and/or functionality) over some time; (ii) the local interactions are not explicitly guided by any external agent; (iii) task-independent objectives are combined with task-dependent constraints.

A number of attempts have been made to formalize aspects of GSO within information theory, thermodynamics and dynamical systems. However, the lack of a broadly applicable mathematical framework across multiple scales and contexts leaves GSO methodology incomplete. Devising such a framework and identifying common principles of guidance are the main themes of the GSO workshops.

Of particular interest are well-founded, but general methods for characterizing GSO systems in a principled way, with the view of ultimately allowing them to be guided toward pre-specified goals. In general, various entropy methods drawing from, and overlapping with, information theory, thermodynamics, nonlinear dynamics and graph theory are relevant, while quantifying complexity and its sources is a common theme.

The key areas of interest will include entropy methods for:


  • information-driven self-organization
  • quantifying complexity
  • self-organizing complex networks
  • adaptive behaviour
  • distributed computation
  • machine learning
  • computational neuroscience
  • swarm intelligence
  • cooperative and modular robotics

Dr. Mikhail Prokopenko
Dr. Carlos Gershenson
Guest Editors

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs).

Published Papers (11 papers)

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Editorial

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Open AccessEditorial Entropy Methods in Guided Self-Organisation
Entropy 2014, 16(10), 5232-5241; doi:10.3390/e16105232
Received: 12 August 2014 / Revised: 22 September 2014 / Accepted: 29 September 2014 / Published: 9 October 2014
Cited by 4 | PDF Full-text (128 KB) | HTML Full-text | XML Full-text
Abstract
Self-organisation occurs in natural phenomena when a spontaneous increase in order is produced by the interactions of elements of a complex system. Thermodynamically, this increase must be offset by production of entropy which, broadly speaking, can be understood as a decrease in [...] Read more.
Self-organisation occurs in natural phenomena when a spontaneous increase in order is produced by the interactions of elements of a complex system. Thermodynamically, this increase must be offset by production of entropy which, broadly speaking, can be understood as a decrease in order. Ideally, self-organisation can be used to guide the system towards a desired regime or state, while "exporting" the entropy to the system's exterior. Thus, Guided Self-Organisation (GSO) attempts to harness the order-inducing potential of self-organisation for specific purposes. Not surprisingly, general methods developed to study entropy can also be applied to guided self-organisation. This special issue covers abroad diversity of GSO approaches which can be classified in three categories: information theory, intelligent agents, and collective behavior. The proposals make another step towards a unifying theory of GSO which promises to impact numerous research fields. Full article
(This article belongs to the Special Issue Entropy Methods in Guided Self-Organization)

Research

Jump to: Editorial

Open AccessArticle Strategic Islands in Economic Games: Isolating Economies From Better Outcomes
Entropy 2014, 16(9), 5102-5121; doi:10.3390/e16095102
Received: 8 July 2014 / Revised: 18 September 2014 / Accepted: 18 September 2014 / Published: 24 September 2014
Cited by 3 | PDF Full-text (1891 KB) | HTML Full-text | XML Full-text
Abstract
Many of the issues we face as a society are made more problematic by the rapidly changing context in which important decisions are made. For example buying a petrol powered car is most advantageous when there are many petrol pumps providing cheap [...] Read more.
Many of the issues we face as a society are made more problematic by the rapidly changing context in which important decisions are made. For example buying a petrol powered car is most advantageous when there are many petrol pumps providing cheap petrol whereas buying an electric car is most advantageous when there are many electrical recharge points or high capacity batteries available. Such collective decision-making is often studied using economic game theory where the focus is on how individuals might reach an agreement regarding the supply and demand for the different energy types. But even if the two parties find a mutually agreeable strategy, as technology and costs change over time, for example through cheaper and more efficient batteries and a more accurate pricing of the total cost of oil consumption, so too do the incentives for the choices buyers and sellers make, the result of which can be the stranding of an industry or even a whole economy on an island of inefficient outcomes. In this article we consider the issue of how changes in the underlying incentives can move us from an optimal economy to a sub-optimal economy while at the same time making it impossible to collectively navigate our way to a better strategy without forcing us to pass through a socially undesirable “tipping point”. We show that different perturbations to underlying incentives results in the creation or destruction of “strategic islands” isolated by disruptive transitions between strategies. The significant result in this work is the illustration that an economy that remains strategically stationary can over time become stranded in a suboptimal outcome from which there is no easy way to put the economy on a path to better outcomes without going through an economic tipping point. Full article
(This article belongs to the Special Issue Entropy Methods in Guided Self-Organization)
Open AccessArticle Complexity and the Emergence of Physical Properties
Entropy 2014, 16(8), 4489-4496; doi:10.3390/e16084489
Received: 30 January 2014 / Revised: 28 May 2014 / Accepted: 4 August 2014 / Published: 11 August 2014
Cited by 4 | PDF Full-text (241 KB) | HTML Full-text | XML Full-text
Abstract
Using the effective complexity measure, proposed by M. Gell-Mann and S. Lloyd, we give a quantitative definition of an emergent property. We use several previous results and properties of this particular information measure closely related to the random features of the entity [...] Read more.
Using the effective complexity measure, proposed by M. Gell-Mann and S. Lloyd, we give a quantitative definition of an emergent property. We use several previous results and properties of this particular information measure closely related to the random features of the entity and its regularities. Full article
(This article belongs to the Special Issue Entropy Methods in Guided Self-Organization)
Open AccessArticle Effects of Anticipation in Individually Motivated Behaviour on Survival and Control in a Multi-Agent Scenario with Resource Constraints
Entropy 2014, 16(6), 3357-3378; doi:10.3390/e16063357
Received: 1 March 2014 / Revised: 21 March 2014 / Accepted: 10 June 2014 / Published: 19 June 2014
Cited by 1 | PDF Full-text (378 KB) | HTML Full-text | XML Full-text
Abstract
Self-organization and survival are inextricably bound to an agent’s ability to control and anticipate its environment. Here we assess both skills when multiple agents compete for a scarce resource. Drawing on insights from psychology, microsociology and control theory, we examine how different [...] Read more.
Self-organization and survival are inextricably bound to an agent’s ability to control and anticipate its environment. Here we assess both skills when multiple agents compete for a scarce resource. Drawing on insights from psychology, microsociology and control theory, we examine how different assumptions about the behaviour of an agent’s peers in the anticipation process affect subjective control and survival strategies. To quantify control and drive behaviour, we use the recently developed information-theoretic quantity of empowerment with the principle of empowerment maximization. In two experiments involving extensive simulations, we show that agents develop risk-seeking, risk-averse and mixed strategies, which correspond to greedy, parsimonious and mixed behaviour. Although the principle of empowerment maximization is highly generic, the emerging strategies are consistent with what one would expect from rational individuals with dedicated utility models. Our results support empowerment maximization as a universal drive for guided self-organization in collective agent systems. Full article
(This article belongs to the Special Issue Entropy Methods in Guided Self-Organization)
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Open AccessArticle Changing the Environment Based on Empowerment as Intrinsic Motivation
Entropy 2014, 16(5), 2789-2819; doi:10.3390/e16052789
Received: 28 February 2014 / Revised: 28 April 2014 / Accepted: 4 May 2014 / Published: 21 May 2014
Cited by 4 | PDF Full-text (1413 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
One aspect of intelligence is the ability to restructure your own environment so that the world you live in becomes more beneficial to you. In this paper we investigate how the information-theoretic measure of agent empowerment can provide a task-independent, intrinsic motivation [...] Read more.
One aspect of intelligence is the ability to restructure your own environment so that the world you live in becomes more beneficial to you. In this paper we investigate how the information-theoretic measure of agent empowerment can provide a task-independent, intrinsic motivation to restructure the world. We show how changes in embodiment and in the environment change the resulting behaviour of the agent and the artefacts left in the world. For this purpose, we introduce an approximation of the established empowerment formalism based on sparse sampling, which is simpler and significantly faster to compute for deterministic dynamics. Sparse sampling also introduces a degree of randomness into the decision making process, which turns out to beneficial for some cases. We then utilize the measure to generate agent behaviour for different agent embodiments in a Minecraft-inspired three dimensional block world. The paradigmatic results demonstrate that empowerment can be used as a suitable generic intrinsic motivation to not only generate actions in given static environments, as shown in the past, but also to modify existing environmental conditions. In doing so, the emerging strategies to modify an agent’s environment turn out to be meaningful to the specific agent capabilities, i.e., de facto to its embodiment. Full article
(This article belongs to the Special Issue Entropy Methods in Guided Self-Organization)
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Open AccessArticle Randomized Binary Consensus with Faulty Agents
Entropy 2014, 16(5), 2820-2838; doi:10.3390/e16052820
Received: 30 January 2014 / Revised: 7 May 2014 / Accepted: 13 May 2014 / Published: 21 May 2014
Cited by 3 | PDF Full-text (712 KB) | HTML Full-text | XML Full-text
Abstract
This paper investigates self-organizing binary majority consensus disturbed by faulty nodes with random and persistent failure. We study consensus in ordered and random networks with noise, message loss and delays. Using computer simulations, we show that: (1) explicit randomization by noise, message [...] Read more.
This paper investigates self-organizing binary majority consensus disturbed by faulty nodes with random and persistent failure. We study consensus in ordered and random networks with noise, message loss and delays. Using computer simulations, we show that: (1) explicit randomization by noise, message loss and topology can increase robustness towards faulty nodes; (2) commonly-used faulty nodes with random failure inhibit consensus less than faulty nodes with persistent failure; and (3) in some cases, such randomly failing faulty nodes can even promote agreement. Full article
(This article belongs to the Special Issue Entropy Methods in Guided Self-Organization)
Open AccessArticle Action-Amplitude Approach to Controlled Entropic Self-Organization
Entropy 2014, 16(5), 2699-2712; doi:10.3390/e16052699
Received: 25 January 2014 / Accepted: 12 May 2014 / Published: 14 May 2014
Cited by 2 | PDF Full-text (1000 KB) | HTML Full-text | XML Full-text
Abstract
Motivated by the notion of perceptual error, as a core concept of the perceptual control theory, we propose an action-amplitude model for controlled entropic self-organization (CESO). We present several aspects of this development that illustrate its explanatory power: (i) a physical view [...] Read more.
Motivated by the notion of perceptual error, as a core concept of the perceptual control theory, we propose an action-amplitude model for controlled entropic self-organization (CESO). We present several aspects of this development that illustrate its explanatory power: (i) a physical view of partition functions and path integrals, as well as entropy and phase transitions; (ii) a global view of functional compositions and commutative diagrams; (iii) a local geometric view of the Kähler–Ricci flow and time-evolution of entropic action; and (iv) a computational view using various path-integral approximations. Full article
(This article belongs to the Special Issue Entropy Methods in Guided Self-Organization)
Open AccessArticle Guided Self-Organization in a Dynamic Embodied System Based on Attractor Selection Mechanism
Entropy 2014, 16(5), 2592-2610; doi:10.3390/e16052592
Received: 10 February 2014 / Revised: 17 April 2014 / Accepted: 22 April 2014 / Published: 13 May 2014
Cited by 5 | PDF Full-text (921 KB) | HTML Full-text | XML Full-text
Abstract
Guided self-organization can be regarded as a paradigm proposed to understand how to guide a self-organizing system towards desirable behaviors, while maintaining its non-deterministic dynamics with emergent features. It is, however, not a trivial problem to guide the self-organizing behavior of physically [...] Read more.
Guided self-organization can be regarded as a paradigm proposed to understand how to guide a self-organizing system towards desirable behaviors, while maintaining its non-deterministic dynamics with emergent features. It is, however, not a trivial problem to guide the self-organizing behavior of physically embodied systems like robots, as the behavioral dynamics are results of interactions among their controller, mechanical dynamics of the body, and the environment. This paper presents a guided self-organization approach for dynamic robots based on a coupling between the system mechanical dynamics with an internal control structure known as the attractor selection mechanism. The mechanism enables the robot to gracefully shift between random and deterministic behaviors, represented by a number of attractors, depending on internally generated stochastic perturbation and sensory input. The robot used in this paper is a simulated curved beam hopping robot: a system with a variety of mechanical dynamics which depends on its actuation frequencies. Despite the simplicity of the approach, it will be shown how the approach regulates the probability of the robot to reach a goal through the interplay among the sensory input, the level of inherent stochastic perturbation, i.e., noise, and the mechanical dynamics. Full article
(This article belongs to the Special Issue Entropy Methods in Guided Self-Organization)
Open AccessArticle Measuring the Complexity of Self-Organizing Traffic Lights
Entropy 2014, 16(5), 2384-2407; doi:10.3390/e16052384
Received: 1 February 2014 / Revised: 15 April 2014 / Accepted: 17 April 2014 / Published: 25 April 2014
Cited by 7 | PDF Full-text (549 KB) | HTML Full-text | XML Full-text
Abstract
We apply measures of complexity, emergence, and self-organization to an urban traffic model for comparing a traditional traffic-light coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and [...] Read more.
We apply measures of complexity, emergence, and self-organization to an urban traffic model for comparing a traditional traffic-light coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only is traffic a non-stationary problem, requiring controllers to adapt constantly; controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures and extending Ashby’s law of requisite variety, we can say that the self-organizing method achieves an adaptability level comparable to that of a living system. Full article
(This article belongs to the Special Issue Entropy Methods in Guided Self-Organization)
Open AccessArticle Intersection Information Based on Common Randomness
Entropy 2014, 16(4), 1985-2000; doi:10.3390/e16041985
Received: 25 October 2013 / Revised: 27 March 2014 / Accepted: 28 March 2014 / Published: 4 April 2014
Cited by 8 | PDF Full-text (516 KB) | HTML Full-text | XML Full-text
Abstract
The introduction of the partial information decomposition generated a flurry of proposals for defining an intersection information that quantifies how much of “the same information” two or more random variables specify about a target random variable. As of yet, none is wholly [...] Read more.
The introduction of the partial information decomposition generated a flurry of proposals for defining an intersection information that quantifies how much of “the same information” two or more random variables specify about a target random variable. As of yet, none is wholly satisfactory. A palatable measure of intersection information would provide a principled way to quantify slippery concepts, such as synergy. Here, we introduce an intersection information measure based on the Gács-Körner common random variable that is the first to satisfy the coveted target monotonicity property. Our measure is imperfect, too, and we suggest directions for improvement. Full article
(This article belongs to the Special Issue Entropy Methods in Guided Self-Organization)
Open AccessArticle Autonomous Search for a Diffusive Source in an Unknown Structured Environment
Entropy 2014, 16(2), 789-813; doi:10.3390/e16020789
Received: 16 December 2013 / Revised: 28 January 2014 / Accepted: 29 January 2014 / Published: 10 February 2014
Cited by 3 | PDF Full-text (413 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The paper presents a framework for autonomous search for a diffusive emitting source of a tracer (e.g., aerosol, gas) in an environment with an unknown map of randomly placed and shaped obstacles. The measurements of the tracer concentration are sporadic, noisy and [...] Read more.
The paper presents a framework for autonomous search for a diffusive emitting source of a tracer (e.g., aerosol, gas) in an environment with an unknown map of randomly placed and shaped obstacles. The measurements of the tracer concentration are sporadic, noisy and without directional information. The search domain is discretised and modelled by a finite two-dimensional lattice. The links in the lattice represent the traversable paths for emitted particles and for the searcher. A missing link in the lattice indicates a blocked path due to an obstacle. The searcher must simultaneously estimate the source parameters, the map of the search domain and its own location within the map. The solution is formulated in the sequential Bayesian framework and implemented as a Rao-Blackwellised particle filter with entropy-reduction motion control. The numerical results demonstrate the concept and its performance. Full article
(This article belongs to the Special Issue Entropy Methods in Guided Self-Organization)

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