Friday, July 4, 2014

On Evolving Self-organizing Technical Systems

Moofushi Kandu fish (Image by Bruno
de Giusti under CC-BY-SA-2.5)
Individual swarm fish behave according to simple rules, which make the overall swarm an efficient entitiy for hunting and avoiding predators. Despite the simple local rules, a school of fish is a working, intelligent system. István Fehervari examined in his doctoral thesis how this behavior can be transferred to technical systems. The result is a tool making it easier to develop self-organizing systems.
Sometimes, it is possible to mimic natural self-organizing behavior for a technical system. At other times, you might not have access to such a template. "If there is no natural system, which we can copy, we have to develop it on our own," says István Fehérvári from the Institute of Networked and Embedded Systems at Alpen-Adria-Universität Klagenfurt. Fehérvári further explains: "This is very difficult because the behavior of a complex system is difficult to predict and the definition of the proper interaction behaviors is hard. Any change in the system creates an effect, often with unwanted consequences. This is why we apply an artificial evolution approach to evolve the local interaction rules,"

To make this approach feasible, the FREVO software has been developed, a tool that helps to apply the evolutionary approach in a unified way to different problem statements and settings. "FREVO provides a one-stop shop, with all the necessary steps for designing a self-organized algorithm for a given problem." says Fehérvári who devoted a major part of his thesis work to design and implement FREVO.  Now the tool is available to other researchers for further experiments and investigations. FREVO is an open-source program in Java that can be freely downloaded at

Further readings and downloads:

Wednesday, June 11, 2014

Simulating the Soccer World Cup 2014

You cannot wait for the soccer World Cup to start? We proudly present a peek preview of the World Cup 2014 - played by teams created with evolutionary algorithms. Using our evolutionary tool FREVO for designing self-organizing systems we have evolved neural networks that make robots playing soccer. During the evolution phase, a fitness function combines different aspects of gameplay like zone defense, man-marking, passing, shots, and goals. By tweaking the weights for these parameters we can influence the playing style of a team while the overall gameplay is still generated automatically by the evolutionary process. Thus we can simulate playing styles of different national teams and then match them against each other.

The following video shows a simulation of Brazil versus Croatia, the opening game of the world cup. The commentary is from Toni Polster, a legendary Austrian soccer player.

While the result is credible, we have not done this to exactly predict the outcome of the games - this would spoil the whole tournament! Furthermore, our approach is not meant for prediction but a system to train a distributed agent-based system to achieve an emergent cooperative behavior in a self-organized way. Setting up this work helped us in improving our understanding how we can create and guide self-organizing systems. We have chosen the soccer simulation as a demonstration because in soccer the global goal (no pun intended) can be achieved in so many different ways , for example with a defensive, offensive, kick-and-rush, pass-intensive, etc. style. And it is nice to watch - who said good science can't be fun!

Further readings:

Thursday, May 22, 2014

Self-Organization for the Smart Grid

In my keynote "AI Techniques for Smart Grids" at the 2014 IEEE Innovative Smart Grid Technologies - Asia conference, I discussed the role and potential of self-organization in the smart grid. Since power systems are critical infrastructure, applying a distributed, bottom-up control structure is a delicate step. On the one hand, self-organizing systems provide very attractive properties like scalability, adaptability and robustness. On the other hand, due to non-linear interactions in the system, small changes that appear insignificant can have severe consequences. To ensure secure operation, this is not the most-wanted property. However, sometimes one gets such systems without a choice. An example is the effect of the unbundling process of power generation and system operation to create a free energy market - a decision that was taken to support distributed on-site power generation with renewable energy sources. At the UCTE grid, the largest synchronous grid in the world in terms of generation capacity, the decreased the scope of control for the Transmission System Operators (TSOs) due to the liberalization of the power market has caused the TSOs to develop a new way of understanding of the system in order to carefully and knowledgeable interact with the system. So we got a self-organization system without explicitly designing the system for being one. Another possibility where self-organization can explicitly support the smart grid is the utilization of smart meters in a way that supports the utilities while still keeping control and private information locally at the customer's side.

To enable self-organization for the smart grid, it is necessary to provide models, proofs, case studies, etc. showing that self-organizing approaches work. For finding self-organizing algorithms we provide our part: with the open-source software tool FREVO, we can evolve agent behavior for self-organizing systems using an automated process based on artificial evolution.


Tuesday, April 29, 2014

Kowloon Walled City - Working Anarchy?
What happens if a large dense settlement is left ungoverned for a long time? This would be the ultimate experiment of a self-organizing social system.

What sounds like an introduction to a Science Fiction story has actually happened in New Kowloon, Hong Kong. After World War II, the Walled City, originally a Chinese military fort became a Chinese enclave within British Hongkong with practically no government enforcement from the Chinese or the British. The Kowloon Walled City became a hotspot of human interaction with about 33000 residents (some estimations even going up to 50000) living at an area of only 2.6 hectars (approximately the size of a football stadium).
The area was made up of 300 interconnected high-rise buildings, built without an architect’s planning. The result was a massive forest of buildings harboring a maze of narrow dark passageways with constant dripping from piping above. Many apartments were windowless. Having no other space, the rooftops only place to breathe fresh air and escape the claustrophobia of the city - even a place for children to play. city was anarchic in its architecture and citizens. Without govermental health and sanitation inspectors, the city attracted sloppy food processors, unlicensed doctors and dentists as well as criminals.

Walled City became a haven for crime and drugs, despite its small area hosting countless brothels, gambling parlors, and opium dens.
Until 1974 the city was run by the Chinese Triads until 1974 with police only venturing in large groups into the city. From 1973-74 on, subsequent police raids helped in cutting back the Triads' power and reducing the crime rate. In 1983, crime was declared to be under control.
Most residents were not involved in any crime and lived peacefully within the Walled City, forming a tightly knit community to help each other and improve daily life there. While sanitary conditions and fire prevention did never achieve an acceptable standard, many other interactions worked surprisingly well in a self-organizing way. Numerous small factories and businesses thrived inside the Walled City.
Despite a reduction in the reported crime rate, both the British and the Chinese governments found the City to be increasingly intolerable. The Hong Kong government announced plans to demolish the Walled City in 1987. Despite the possibility of a financial compensation to citizens and businesses, this was not to the delight of some of the citizens. After a long protracted eviction process, demolition began in March 1993 and was completed in April 1994. Today, the area is occupied by the Kowloon Walled City Park.

Thumbnails are based on photos from the Canadian photographer Greg Girard who, in collaboration with book author Ian Lambot, spent five years in Kowloon Walled City to get to know the residents and to learn how it was (self-)organized. Arial view of Kowloon City by Stevage under GFDL 1.2.

Sunday, April 20, 2014

The Next Big Thing in Artificial Evolution

As announced in a previous blogpost, Prof. A. E. Eiben gave a very interesting talk on the next big in thing in artificial evolution during his visit at the Alpen-Adria-Universität Klagenfurt. Eiben presented a vision about having animate artefacts that are able to evolve and self-reproduce in physical spaces. To make this happen, he gives a notion of the integration of "hard" vs. "soft" evolutionary computation, the former meaning evolutionary optimiziation and design while the latter refering to artificial life, swarm robotics, and artificial societies.

Gusz Eiben's talk was attracting many people and lead to a vivid discussion afterwards about technology, possibilities, societal implications and parallels to existing sci-fi stories from Philip K. Dick or movies such as Terminator. So I think it is appropriate to say this talk was truly presenting science beyond fiction.

Tuesday, April 15, 2014

How the body affects the mind - On the effects of robot configuration on evolved behavior

The design of robotic controllers through evolutionary methods requires making a large number of choices about the experimental setup, which are often left to the expertise or naïveté of the experimenter. Although much attention is normally given to the fitness function or the genotype-to-phenotype mapping determining the robot controller, the robot configuration is often selected with little care. Yet, an ill-defined configuration - in terms of the selected subset of the sensory-motor system, or in the pre-processing of the raw sensor data - may be decisive in determining the failure of the evolutionary process.

Different emerged patterns 
simulated with ARGoS
In our paper "On the effects of the robot configuration on evolving coordinated motion behaviors" we studied the effect of different robot configurations on the ability to evolve efficient behaviors for a swarm robotics system. In this domain, the choice of a good configuration is fundamental as even small details can lead to large differences in the group behavior. To demonstrate the importance of the robot configuration, we test different alternatives and measure the group performance on a bi-objective scale.

The results show that different configurations not only have a strong effect on performance, but they also correspond to behaviors with radically different features concerning the organization of the group.

The following video illustrates three basic behaviors that emerged: wavefront, train and flocking:

For more information, see:

I. Fehérvári, V. Trianni, and W. Elmenreich. On the effects of the robot configuration on evolving coordinated motion behaviors. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE, June 2013.

Wednesday, April 9, 2014

Prof. A. E. Eiben at Lakeside Labs: The Next Big Thing in Artificial Evolution

Professor Àgoston E. Eiben will give an interesting talk at the Lakeside Labs / Alpen-Adria-Universität Klagenfurt on April 10th 2014, 15:00 CET, Room L4.1.114  

This talk presents a vision about the upcoming breakthrough in artificial evolution: animate artefacts that (self-)reproduce in physical spaces. In other words, we witness the “Evolution of Things”, rather than just the evolution of digital objects, leading to a new field of Embodied Artificial Evolution. After presenting this vision some of the technical challenges are elaborated and related to the main algorithmic/technical requirements to the current know-how in evolutionary computing. Finally, Prof. Eiben will speculate about possible applications, their societal impacts, and argue that these developments will radically change our lives.

For those who cannot attend or want to warm up on the topic, we recommend Eiben's TED talk "Evolution at Work":

A.E. Eiben is a professor of Computational Intelligence on the VU University Amsterdam and Visiting Professor in the Department of Electronics of the University of York, UK. He is one of the European early birds of Evolutionary Computing; his first EC paper dates back to 1989 and he co-authored the first comprehensive book on the subject. He has been organizing committee member of practically all major international evolutionary computing conferences and editorial board member of related international journals. He have also coordinated or participated in several EU research projects. Prominent themes in his work include multi-parent recombination methods, evolutionary constraint handling, evolutionary art, artificial life, and evolutionary robotics. Furthermore, he is concerned with methodological issues, especially the design and calibration of evolutionary algorithms (parameter tuning off-line as well as parameter control on-line). Lately he became interested in artificial evolutionary systems that are physically embodied in real time and real space. This goes far beyond conventional evolutionary computing in digital spaces and implies great new opportunities and challenges – see his TEDx talk and a journal paper on the vision, and the The Triangle of Life framework for a possible implementation in robotic systems that can self-reproduce. On the long term, a broad range of possible “incarnations” can emerge and form a radically new way of engineering. Furthermore, they can serve as an apparatus to investigate deep scientific questions about evolution in a new substrate, different from carbon-based life as we know it.