New research line on µRTS

This new research line was started one year ago together with PhD student Abdessamed Ouessai and professor Mohammed Salem both from the University of Mascara (Algeria).

The objective is focused on the improvemet of the decision process of an autonomous agent for playing a simple Real-Time Strategy Game, named µRTS (microRTS). See an illustrative image below of this game/simulator created by Proffessor Santiago Ontañón mainly for research purposes.

For the moment three papers have been published:

  1. Online Adversarial Planning in μRTS: A Survey. Presented at 2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS) on December 2019, and selected as Best paper of the conference.
  2. Improving the Performance of MCTS-Based µRTS Agents Through Move Pruning. Published and presented at IEEE Conference on Games 2020 last August 2020.
  3. Parametric Action Pre-Selection for MCTS in Real-Time Strategy Games. Presented at CoSECiVi 2020 yesterday.

Here you can see the slides and the video presentation of the paper at CoG:

Moreover an agent named USMBot was created and participed in the last µRTS AI Competition, reaching rank 4!!!

The bot can be found in Github

We hope you like it, as usual. :D

Angry Birds meet EAs at EVO* 2019

Last 24 of April we presented the work “Free Form Evolution for Angry Birds Level Generation” at EVOApplications 2019 (EvoGAMES) a conference part of EVO* 2019, held in Leipzig (Germany).

The abstract of the work is:

This paper presents an original approach for building structures that are stable under gravity for the physics-based puzzle game Angry Birds, with the ultimate objective of creating fun and aesthetically pleasing Angry Birds levels with the minimum number of constraints. This approach consists of a search-based procedural level generation method that uses evolutionary algorithms. In order to evaluate the stability of the levels, they are executed in an adaptation of an open source version of the game called Science Birds. In the same way, an open source evolutionary computation framework has been implemented to fit the requirements of the problem. The main challenge has been to design a fitness function that, first, avoids if possible the actual execution of the simulator, which is time consuming, and, then, to take into account the different ways in which a structure is not structurally sound and consider them in different ways to provide a smooth landscape that eventually achieves that soundness. Different representations and operators have been considered and studied. In order to test the method four experiments have been carried out, obtaining a variety of stable structures, which is the first path for the generation of levels that are aesthetically pleasing as well as playable.

@amorag did a short presentation and later ‘defended’ a poster during the reception act. The presentation is a description of the poster:

Actually the poster was selected as the second best of the conference by the attendants. :D

Those interested can found the paper at Springer web:

Enjoy it… and cite us! ;D

Aplicando algoritmos genéticos a un controlador ‘fuzzy’ para una gestión adaptativa del tráfico

Recientemente se ha aceptado el artículo “A hybrid Fuzzy Genetic Algorithm for an adaptive traffic signal system” en la revista open-access Advances in Fuzzy Systems. En él participamos varios de los investigadores de GeNeura.

El abstract es:

This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC.

Esperamos que os guste (y que lo citéis, claro :D).

Finding an evolutionary solution to the game of Mastermind with good scaling behavior

As important as finding a solution to the game of MasterMind that is better than anyone else is to find one that can be applied to a wide range of sizes. In this paper we get rid of a parameter, the limit size of the consistent set we use for scoring every combination. This makes a faster algorithm, and not always worse than the optimal consistent set size.

This was the paper presented at LION by Antonio Fernández using this presentation