Monday, December 1, 2014

Advent Programming Contest 2014

Still in the flow from the IEEEXtreme programming challenge? Looking for a daily new programming problems to train your skills?
The Advent Programming Contest 2014, organized by the IEEE Student Branch Klagenfurt will provide a new problem every day from December 1st to December 24th. On Saturdays and Sundays, new problems will appear at 12:00 Central European Time, on workdays at 18:00 CET. You can submit solutions any day until the contest ends on December 26. You can choose to use C, C++, Java, Python or Perl as programming language. The programming tasks can be solved with short programs (typically less than 100 lines of code). Until a solution is correct you can submit your program as often as you want (but please don't spam our server). Your score depends on the number of correct solutions and the time and trials you needed to solve the problem. Winners will be announced after closing of the contest.

The event is open to everyone. If you want to participate, please register at When you register please indicate if you belong to the group University, Pupils or other.
This is an individuals competition, not a team contest - be fair!
You can also join the contest after 1st December, registration is possible until December 24.

Monday, November 24, 2014

Scalability in Self-Organizing Systems

One of the properties of self-organizing systems is scalability. It means that system keeps its working capabilities even if we remove some of its components or add more of them. In our reseach, we employ different evolutionary algorithms (EAs) to create a self-organizing system. In particular, algorithms like a simple evolutionary algorithm or a two dimensional cellular EA are used  for adjusting the synaptic weights of an neural controller. The best solutions are identified based on simulations of the target application. Typically, the simulation parameters limit the applicability of the solution - there is no guarantee that an evolved solution is adaptable or scalable to situations not specified in the simulation parameters. On the other hand, there are many examples in nature where solutions could be successfully employed in other contexts. We decided to check how our soccer teams, which consist of evolved neural controllers, can scale.

For the FIFA World Cup in Brazil we organized our own tournament between evolved self-organized soccer teams. This is an exciting show - to see how simple agents having only partial information about the environment around them are reaching its goal (score a goal) as a team. Will they be able to play in the same manner if we take the contoller, trained in the simulation with 10 players per team, and increase or decrease the number of players? This question has remained open until today.

In our first scenario, we assume that we invited two soccer teams to show us a fantastic game, but due to some circumstances, only 4 players per teamshow up.
Thus our first experiment can be seen in the video below.
Despite the players being evolved in a context of 11 players on each side, reducing the number of players did not affect the ability of players to show good game.

To check the other extreme, we settled a very dangerous experiment - each team consisting of 40 players! The results were stunning (see video below). These soccer heroes could play as a team even with significantly increased number of players. Unfortunately, they could not play for a long time in this mode: Marco Materazzi headbutted Zinadine Zidane in the chest and shouted "Revenge!"; Luis Suarez bit two players in order to show his perfect teeth; Diego Maradona scored the goal by striking the ball with his hand and this time he was disqualified for this trick. We didn't care about these incidents since we got the results of our experiment:


Sunday, October 19, 2014

On the Road to your PhD

In the blog Between a rock and a hard place, James Hickey posted a valuable list of tipps for succeeding in your PhD:

1. Learn Latex
2. Use Bibtex
3. Keep your papers organised
4. Keep a formatted list of your own publications and conference abstracts as you go along
5. Always give conference abstracts different titles
6. Keep on top of your emails
7. Manage time
8. Hypothesis testing
9. Keep detailed notes
10. Avoid perfectionism
11. Always give deadlines when you want feedback
12. Source additional funding
13. Write as you go
14. Don’t be scared of your supervisors
15. Log out of Facebook
16. Keep an eye on your budget
17. Diversify yourself
18. Music
19. Get your workstation set up
20. Take notes in meetings
21. Read around your subject
22. Write a literature review
23. Socialise!
24. Sport and/or hobbies
25. Go to conferences and workshops
26. Network
27. Establish a routine
28. Take the lead
29. Practice presenting your work
30. Be prepared for the worse
31. Back up, and back up again
32. Small steps to success
33. Keep on top of admin

Being somebody who already has his PhD, I find the list very useful, the title "Things I wish I knew when I started my PhD..." definitely holds some truth, although I accidently did many of the good things mentioned there. See the original post for a detailed explanation of every item!

Sunday, October 12, 2014

Self-organizing Processes in Physical Geography

In this talk, Marco van der Wiel presents some ideas on self-organization and self-organized criticality, and how these relate to physical geography and (explanatory and exploratory) modelling in physical geography.

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The talk is in english, you might want to skip the german introduction until 1:40. Video by AAU Campus TV/Egmont Sparouz.

Tuesday, August 19, 2014

DarwinTunes - Evolution of Music by Public Choice
The natural world – creatures, plants, infections – is the result of Darwinian evolution by natural selection, i.e., the gradual process of (biological) traits become either more or less common in a population based on the success of the organism carrying these traits. This process, repeated for two billion years, has created the vast diversity of life on earth.
The same process can be also observed in human society where cultural artifacts – words, songs, images, ideas – are constantly remixed and reinterpreted by people. A reinterpretation is an imperfect copy and therefore a “mutation”. Thus, the variety of our culture is the result of a cultural evolution.
In order to examine the underlying mechanism of cultural evolution, Robert M. MacCalluma, Matthias Mauch, Austin Burta, and Armand M. Leroia constructed a Darwinian music engine consisting of a population of short audio loops that sexually reproduce and mutate.
The selection is based on human feedback via a webpage that implements the remixing of tunes as a game. By remixing your tune with others, the other parent gets a score point. Your goal is to make an attractive tune - the more often you get remixed, the more points you have.

In 2010, researchers from the Alpen-Adria-Universität Klagenfurt released a similar system where people could vote for recombinations of music tunes.  


Sunday, August 10, 2014

Tenure Track Research Professor Position in Big Data at UNAM

The Computer Science Department of the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) of the Universidad Nacional Autónoma de México (UNAM) has a open call for a tenure-track research professor in big data.

Located in the heart of the UNAM's Ciudad Universitaria, a UNESCO World Heritage site, the IIMAS has been the leader in computer science in Mexico since the first computer in the country was acquired by UNAM. Researchers at UNAM have a privileged position for several reasons. UNAM is the highest ranked spanish speaking higher education institution in the world and produces half of the research in Mexico and is the largest in the continent (300K+ students). Professors in faculties do more teaching than research, while researchers in institutes (such as IIMAS) do more research than teaching (about 48 hours per year, usually to the best graduate students in the country. Groups of more than five students get a teaching assistant). Students in most graduate programs at UNAM receive automatically a scholarship, and there is travel budget for researchers, minimizing the grant writing load. There are several grant calls with high acceptance rates. There are also two postdoctoral fellowship calls per year internal to UNAM. High performing researchers can reach tenure in less than five years.

Requirements for this call are:

  1. To be less than 39 (women) or 37 (men) years old.
  2. To have a PhD degree in computer science, statistics, or related areas from a renowned institution.
  3. To have published high quality research papers related to big data.
  4. To have teaching skills at undergraduate and graduate levels.
  5. To have abilities to direct undergraduate and graduate theses.
  6. To be able to collaborate in multidisciplinary research projects.
  7. To fulfill the norms described at
Interested candidates should send the following documentation to Dr. Ricardo Berlanga (berlanga "at", Academic Secretary of IIMAS before October 7th, 2014:
  1. Application request (in Spanish).
  2. Updated Curriculum Vitae, including publication list (in Spanish).
  3. Copy of PhD degree.
  4. Copy of publications.
  5. At least two references with email included.
  6. A work plan which includes research and teaching prospects for the next three years (in Spanish.
Selected candidates will be invited to give an open talk or videoconference at IIMAS. An ad hoc commission will make a final decision.
More information can be requested to:
Dr. Carlos Gershenson
Head of Computer Science Department, IIMAS, UNAM
cgg "at"

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.

Wednesday, March 5, 2014

Solving the „Tracking Game“

Guest article from Doris A. Behrens

Games taking place in a shared environment are characterized by the fact that the effectiveness of individual decisions heavily depends on the decisions of other players. Our algorithm OPTGAME is able to approximate the evolution of choices to be made if a number of independent decision makers seek to reach individually desirable states. The evolution of states subject to control is described by a system of nonlinear difference equations. We call this a „tracking game“, since is an extension of the linear regulator problem (also known as „tracking problem“) that is well known from LQ optimal control theory.
OPTGAME is a tool that steers the control and state paths towards desired outcomes. It is novel in a way that it works for game theoretic systems with nonlinear constraints. It searches for equilibrium solutions by iteratively applying a sequence of local linearization and optimization over the entire planning horizon. The tool yields three types of non-cooperative equilibrium solutions (open-loop Nash equilibrium, feedback Nash equilibrium, feedback Stackelberg equilibrium) plus one cooperative solution (Pareto-optimal strategy).
An example for such a game could be the decision-making within a monetary union such as the European Monetary Union (EMU). In this game all but one player represent countries with intentions for economic growth, employment and limited budget deficit and one player represents the European Central Bank, aiming solely at price stability. Besides trade-offs between state variables, for example the well-known trade-off between unemployment and price stability (see Phillips curve), there are strong economic interdependencies due to international trade.
European Monetary Union
For instance, if a single country aims at economic growth, one option could be to increase the demand for goods and services from the public sector. This increases production in response to demand, which in turn increases incomes. However, within an open economy the future success strongly depends on the situation and behavior of the other member countries. In order to find a solution for this problem it is necessary to estimate the countries’ individual parameters as well as the degree of economic interdependencies (like trade) between countries.
Such models, in order to be accurate are inherently nonlinear, which cannot be solved analytically by a linear model such as the LQ game. In our work we apply OPTGAME to a monetary union macroeconomic model based on the nonlinear MUMOD1 model. In this model, there are basically two groups of countries, one economically stronger than the other, all experiencing a brief period of recession.
Doris A. Behrens is a senior re-
searcher working on optimization
in techno-socio-economic systems
at the Alpen-Adria-Universität
Without policy intervention all countries would experience a deep recession and an enormous increase in public debt. By applying OPTGAME for different solution concepts we learn that macroeconomic properties like public debt, economic growth, inflation, etc. can be significantly improved with system-aware control actions of players.

The OPTGAME tool is available as MATLAB implementation upon request (Contact Doris A. Behrens).


Doris A. Behrens, Reinhard Neck, Approximating Solutions for Nonlinear Dynamic Tracking Games, Computational Economics, Springer, February 2014. DOI: 10.1007/s10614-014-9420-4

Reinhard Neck, Doris A. Behrens, A macroeconomic policy game for a monetary union with adaptive expectations. Atlantic Economic Journal, 37(4), 335–349, 2009. DOI: 10.1007/s11293-009-9186-6f