Month: January 2019

Complexity and Self-Organization | Frontiers Research Topic

Complexity occurs when relevant interactions prevent the study of elements of a system in isolation. These interactions between elements may lead to the self-organization of the system. In computational intelligence, complexity and self-organization have been studied and exploited with different purposes. This Research Topic aims to bring together novel research into a coherent collection, spanning from theory and methods to simulations and applications.

Computational measures of complexity and self-organization have been proposed and applied to study a broad range of phenomena. Methodologies for facing complexity and harnessing self-organization have been used to design and build a variety of systems. Computer simulations have been tools which enabled us to study complexity and self-organization, from cellular automata and artificial neural networks to multi-agent systems and computational social science. The applications of these approaches have been vast.

Considering that complexity and self-organization are very broad themes, this Research Topic focusses only on the aspects related to computational intelligence.


Submission Deadlines
31 July 2019 Abstract
30 September 2019 Manuscript


Complex Systems Summer School | Santa Fe Institute

The SFI Complex Systems Summer School (CSSS) offers an intensive 4-week introduction to complex behavior in mathematical, physical, living, and social systems. Lectures are taught by the faculty of the Santa Fe Institute (SFI) and other leading educators and scholars. The school is for graduate students, postdoctoral fellows, and professionals seeking to transcend traditional disciplinary boundaries, take intellectual risks, and ask big questions about complex systems.

The program consists of an intensive series of lectures, labs, and discussions focusing on foundational concepts, tools, and current topics in complexity science. These include nonlinear dynamics, scaling theory, information theory, adaptation and evolution, networks, machine learning, agent-based models, and other topical areas and case studies. Participants collaborate in developing novel research projects throughout the four weeks of the program that culminate in final presentations and papers. 


Begins: Jun 09 2019
Ends: Jul 05 2019

Deadline extension: now Thursday, January 31.


Causal deconvolution by algorithmic generative models

New paper in Nature Machine Intelligence and a video produced by Nature shows how small programs can help deconvolve signals and data: and


"Most machine learning approaches extract statistical features from data, rather than the underlying causal mechanisms. A different approach analyses information in a general way by extracting recursive patterns from data using generative models under the paradigm of computability and algorithmic information theory.


Complex behaviour emerges from interactions between objects produced by different generating mechanisms. Yet to decode their causal origin(s) from observations remains one of the most fundamental challenges in science. This paper introduces a universal, unsupervised and parameter-free model-oriented approach, based on the seminal concept and the first principles of algorithmic probability, to decompose an observation into its most likely algorithmic generative models."


Morphogenesis in robot swarms

Morphogenesis allows millions of cells to self-organize into intricate structures with a wide variety of functional shapes during embryonic development. This process emerges from local interactions of cells under the control of gene circuits that are identical in every cell, robust to intrinsic noise, and adaptable to changing environments. Constructing human technology with these properties presents an important opportunity in swarm robotic applications ranging from construction to exploration. Morphogenesis in nature may use two different approaches: hierarchical, top-down control or spontaneously self-organizing dynamics such as reaction-diffusion Turing patterns. Here, we provide a demonstration of purely self-organizing behaviors to create emergent morphologies in large swarms of real robots. The robots achieve this collective organization without any self-localization and instead rely entirely on local interactions with neighbors. Results show swarms of 300 robots that self-construct organic and adaptable shapes that are robust to damage. This is a step toward the emergence of functional shape formation in robot swarms following principles of self-organized morphogenetic engineering.


Morphogenesis in robot swarms
I. Slavkov, D. Carrillo-Zapata, N. Carranza, X. Diego, F. Jansson, J. Kaandorp, S. Hauert, and J. Sharpe

Science Robotics 19 Dec 2018:
Vol. 3, Issue 25, eaau9178
DOI: 10.1126/scirobotics.aau9178