EvoGAMES is coming… Check out the CFP

The deadline for submitting your paper to EvoGAMES (and the rest of Evo*) is now set (1 November).

EvoGAMES is a track of the European Conference on the Applications of Evolutionary Computation focused on the applications of bio-inspired algorithms in games.

The areas of interest for the track include, among others:
Computational Intelligence in video games
  – Intelligent avatars and new forms of player interaction
  – Player experience measurement and optimization
  – Procedural content generation
  – Human-like artificial adversaries and emotion modelling
  – Authentic movement, believable multi-agent control
  – Experimental methods for gameplay evaluation
  – Evolutionary testing and debugging of games
  – Adaptive and interactive narrative and cinematography
  – Games related to social, economic, and financial simulations
  – Adaptive educational, serious and/or social games
  – General game intelligence (e.g. general purpose drop-n-play Non-Player Characters, NPCs)
  – Monte-Carlo tree search (MCTS)
  – Affective computing in Games

Important dates are:
– Submission of papers: 1 November 2015
– Notification: 4 January 2015
– Camera-ready: 18 January 2015
– Evo* dates: 30 March – 1 April 2016

This year, the page limit has been increased up to 16 pages, so you could write more and more scientific content. :D

As usual, the accepted submissions will be included in the proceedings of Evo*, published in a volume of the Springer Lecture Notes in Computer Science.

For more info about the conference and the track you can visit the Main site of Evo* 2016.

See you in Porto!


Evostar 2015 mandatory post

We can never skip the chance to assist the Evostar conference, and aside learn the latest trends in Evolutionary Computation and present our results, we also have a good time with our other colleagues.

This time the conference was held in Copenhagen (Denmark), and because Antonio and me were part of the organization we didn’t have much time to go sightseeing, but we went to Tivoli Gardens and ride the flying chairs (and screamed like babies).

On the scientific part, we presented two papers to EvoGames track, related with our research lines on content generation for videogames and AI optimization. The first paper, How the World Was MADE: Parametrization of Evolved Agent-Based Models for Backstory Generation, presents a study on parametrization of the values that define a virtual world to facilitate the emergence of archetypes, and be able to generate interesting backstories (for videogames, for example). See the poster here:

The poster

Also, as we are commited to open science and open software, you can download the MADE environment from its web. The abstract:

Generating fiction environments for a multi-agent system optimized by genetic algorithms (with some specific requirements related to the desirable plots), presents two main problems: first it is impossible to know in advance the optimal value for the particular designed fitness function, and at the same time, it creates a vast search space for the parameters that it needs. The purpose of this paper is to define a methodology to find the best parameter values for both, the evolutionary algorithm, and the own fictional world configuration. This design includes running, to completion, a world simulation represented as a chromosome, and assigning a fitness to it, thus composing a very complex fitness landscape.
In order to optimize the resources allocated to evolution and to have some guarantees that the final result will be close to the optimum, we systematically analyze a set of possible values of the most relevant parameters, obtaining a set of generic rules. These rules, when applied to the plot requisites, and thus, to the fitness function, will lead to a reduced range of parameter values that will help the storyteller to create optimal worlds with a reduced computation budget.

Evostar 2015 - Copenhagen(That’s me with the IKEA rat plushies I used to describe our system)

Our other paper, It’s Time to Stop: A Comparison of Termination Conditions in the Evolution of Game Bots, describes a methodology to compare different termination conditions in noisy environments such as the RTS games. The abstract:

Evolutionary Algorithms (EAs) are frequently used as a mechanism for the optimization of autonomous agents in games (bots), but knowing when to stop the evolution, when the bots are good enough, is not as easy as it would a priori seem. The first issue is that optimal bots are either unknown (and thus unusable as termination condition) or unreachable. In most EAs trying to find optimal bots fitness is evaluated through game playing. Many times it is found to be noisy, making its use as a termination condition also complicated. A fixed amount of evaluations or, in the case of games, a certain level of victories does not guarantee an optimal result. Thus the main objective of this paper is to test several termination conditions in order to find the one that yields optimal solutions within a restricted amount of time, and that allows researchers to compare different EAs as fairly as possible. To achieve this we will examine several ways of finishing an EA who is finding an optimal bot design process for a particular game, Planet Wars in this case, with the characteristics described above, determining the capabilities of every one of them and, eventually, selecting one for future designs.

Evostar 2015 - Copenhagen(Here’s Antonio presenting the paper)

You can see the rest of the Evostar photos in their flickr account.

Conferencia de Alberto Tonda en la ETSIIT-UGR (20 de enero de 2015)

Dentro de la visita de investigación financiada con el premio al mejor artículo del congreso EvoStar2014 ofrecido por el Programa GENIL (Granada Excellence Network of Innovation Laboratories) asociado al CEI BioTic GRANADA, Alberto Tonda impartirá la conferencia con título “Learning Dynamical Systems with Symbolic Regression” en la ETSIIT-UGR.

Fecha: Martes, 20 de enero de 2015
Hora: 12:30
Lugar: Salón de Grados (ETSIIT)

Title: Learning Dynamical Systems with Symbolic Regression
Author: Alberto Tonda (INRA)


Dynamical Systems, composed of Ordinary Differential Equations (ODE), are widely adopted in many fields of science and engineering, to describe phenomena ranging from fluid dynamics to diffusion of heat, from mechanical to biological processes. ODE-based models are usually built by human experts, exploiting both experimental data and theoretical knowledge of the problem at hand.

In this work, we show how to use an established machine learning technique, Symbolic Regression, to automatically derive systems of ODEs starting from data. Preliminary experimental results on the Lotka-Volterra model prove the potential of the approach.

Alberto Tonda is a researcher at INRA (the French national institute for agronomy), where he works on semi-supervised modeling of food processes. He completed his PhD in Computer Science at Politecnico di Torino, Italy, in 2010, with a thesis on real-world applications of Evolutionary Computation.

Is entropy good for solving the game of MasterMind?

Well, it does. In another paper published in the Evostar conference, we compare several methods for measuring how good a combination is when compared to the others that could possibly be the solution; so far we had mostly used most parts (counting the number of non-zero partitions), but, in this paper, that compares our previous Evo method with another created by the coauthors, Maestro-Montojo and Salcedo-Sanz, we find that Entropy, at least for these sizes, is the way to go. Here’s the poster

You can access the paper Comparing Evolutionary Algorithms to Solve the Game of MasterMind, by Javier Maestro-Montojo, Juan Julián Merelo and Sancho Salcedo-Sanz (first and last authors from the University of Alcalá de Henares) online or request a copy from the authors.

Testing different diversity-enhacing migration and replacement policies in dynamic environments (an evolutionary robotics case)

The paper “Testing Diversity-Enhancing Migration Policies for Hybrid On-Line Evolution of Robot Controllers” has been published in Evostar 2012. This work was developed during my foreign stay at the Vrije Universiteit Amsterdam, with Doctor A.E. Eiben. Appart from having a great time of my life in Amsterdam, I did experiments, and science and stuff.

In this work, we present the results obtained from comparing several migration policies that tries to optimize in a noisy fitness environment: the on-line, on-board and hybrid evolutionary robotics problem. Three different migration policies have been studied (the most different migrant, random migrant and best migrant) and two replacement mechanisms: the migrant replaces the worst, or the migrant replaces the worst after being evaluated only if is better. Experiments with 4, 16 and 36 robots were conduced, with two different topologies (ring and panmictic) and also a comparison with other evolutionary robotics algorithms were performed. Results show that the replacement mechanism has more influence than the migration policy or topology, and it also affects the tuning of the algorithm parameters. We asked ourselves the next questions:

  • Using the hybrid approach (island model), which is the best combination of migration policy, admission policy, and island topology?
  • Is this combination better than the encapsulated and distributed alternatives?
  • Does the number of robots affect the result and if so, how?

Conclusions, graphs and stuff and in the paper, but summarizing, multikulti technique (receive the most different individual of my population from other islands) and accept it in my population after its evaluation perform better than other alternatives, even with less migration rate.

You can also check the poster here.

The Springer link to the paper is  Testing Diversity-Enhancing Migration Policies for Hybrid On-Line Evolution of Robot Controllers but you can download the draft.

The abstract:

We investigate on-line on-board evolution of robot controllers based on the so-called hybrid approach (island-based). Inherently to this approach each robot hosts a population (island) of evolving controllers and exchanges controllers with other robots at certain times. We compare different exchange (migration) policies in order to optimize this evolutionary system and compare the best hybrid setup with the encapsulated and distributed alternatives. We conclude that adding a difference-based migrant selection scheme increases the performance.