About Antonio Mora

Informático y Loeño de pura cepa

Improved Genetic Fuzzy Drivers presented at CIG 2018

Last week I presented at IEEE CIG 2018 (held in Maastricht, The Netherlands) our following step in our research about autonomous drivers for Car Racing Simulators, such as TORCS, titled “The Evolutionary Race: Improving the Process of Evaluating Car Controllers in Racing Simulators“.

As commented before by @jjmerelo and later by @fergunet, we designed with Mohammed Salem (University of Mascara) a driver’s AI in which two Fuzzy Subcontrollers were hybridized with a Genetic Algorithm.

In this work we present a better evaluation approach for the GA, combining three methods: heuristic track choosing, improved fitness functions, and race-based selection of the best.

The abstract of the work is:

Simulated car races have been used for a long time as an environment where car controlling algorithms can be tested; they are an interesting testbed for all kinds of algorithms, including metaheuristics such as evolutionary algorithms. However, the challenge in the evolutionary algorithms is to design a reliable and effective evaluation process for the individuals that eventually translates into good solutions to the car racing problem: finding a controller that is able to win in a wide range of tracks and with a good quantity of opponents. Evaluating individual car controllers involves not only the design of a proper fitness function representing how good the car controller would be in a competitive race, but also the selection of the best solution for the optimization problem being solved; this decision might not be easy when uncertainty is present in the problem environment; in this case, weather and track conditions as well as unpredictable behavior of other drivers. Creating a methodology for the automatic design of the controller of an autonomous driver for a car racing simulator such as TORCS is an optimization problem which offers all these challenges. Thus, in this paper we describe an analysis and some proposals to improve the evaluation of optimized fuzzy drivers for TORCS over previous attempts to do so. It builds on preliminary results obtained in previous papers as a baseline and aims to obtain a more competitive autonomous driver via redesign of the fitness evaluation procedure; to this end, two different fitness functions are studied in several experiments, along with a novel race-based approach for the selection of the best individual in the evolution.

And the presentation is:

You can check our paper in the proceedings of the conference.

Enjoy it!

(And cite us as usual :D)

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Our TORCS driving controller presented at EvoGAMES 2017

Last week, @jjmerelo presented at EvoGAMES 2017 (inside Evo* 2017) our work titled “Driving in TORCS using modular fuzzy controllers”.

This paper presents a novel car racing controller for TORCS (The Open Racing Car Simulator), which is based in the combination of two fuzzy subcontrollers, one for setting the speed, and one to control the steer angle. The obtained results are quite promissing, as the controller is quite competitive even against very tough TORCS teams.

The abstract of the paper is:

When driving a car it is essential to take into account all possible factors; even more so when, like in the TORCS simulated race game, the objective is not only to avoid collisions, but also to win the race within a limited budget. In this paper, we present the design of an autonomous driver for racing car in a simulated race. Unlike previous controllers, that only used fuzzy logic approaches for either acceleration or steering, the proposed driver uses simultaneously two fuzzy controllers for steering and computing the target speed of the car at every moment of the race. They use the track border sensors as inputs and besides, for enhanced safety, it has also taken into account the relative position of the other competitors. The proposed fuzzy driver is evaluated in practise and timed races giving good results across a wide variety of racing tracks, mainly those that have many turning points.

There was an interactive presentation at the conference, together with a poster:

The paper is available online from: https://link.springer.com/chapter/10.1007/978-3-319-55849-3_24

Enjoy (and cite) it! :D

 

Mathematics applied to the maintenance of radio communication devices

Yesterday, Alexander Lyubchenko, post-doctoral researcher from Omsk State Transport University, made a report on the topic “Mathematical support for preventive maintenance periodicity optimization of radio communication facilities”.

The presentation was:

He presented the results of his research field and shared with us ideas for future work.

A small discussion took place concerning the application of another research approaches for solving the presented task, which could provide better efficiency and accuracy of calculations… Somebody said Evolutionary Algorithms? Yes, of course! :D

Thus, it is possible to conclude that the organised event was productive.

Alexander is doing a short visit to our group until next June.

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).

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!

Charla sobre Ciencia y Videojuegos en la Universidad de Cádiz

El viernes pasado, Pablo García (aka Dr. Fergu) y yo mismo (aka Dr. Mora) dimos con éxito la charla “La Ciencia y los Videojuegos” en la Escuela Superior de Ingeniería de la Universidad de Cádiz.

Fuimos invitados por el Dr. Manuel Palomo Duarte y muy bien acogidos, tanto por él, como por el resto de sus compañeros. ;D

En la primera parte, conté la relación existente entre ciencia y videojuegos (con mi clásica presentación), con una parte centrada en la interacción autoática y la extracción de información en videojuegos para la creación o mejora de aspectos de los mismos (Inteligencia Artificial, Generación Procedural de Contenidos, etc).

La presentación es:

En la segunda parte, Pablo explicó varios de los desarrollos e investigación del grupo GeNeura en el ámbito de los videojuegos, aplicando Algoritmos Evolutivos a juegos como Unreal, Planet Wars, Super Mario o StarCraft.

Su presentación es:

Esperamos que os gustase (u os guste, si las veis ahora). ;)

Ms. PacMan in IEEE Transactions on CI and AI in Games

Our fans and followers must be happy! ;D

They can now access the excellent work by Federico Liberatore in IEEE ToCIAIG journal.

This is the best journal concerning Artificial Intelligence in games, with a very strict reviewing process, so, we are very proud of this success. ;)

This is the next step in the research started one year and a half ago designing competitive  Ghost Teams for catching Ms. PacMan.

The abstract is:

In the last year, thanks to the Ms. Pac-Man vs Ghosts competition, the game of Ms. Pac-Man has gained increasing attention from academics in the field of Computational Intelligence. In this work, we contribute to this research stream by presenting a simple Genetic Algorithm with Lexicographic Ranking (GALR) for the optimization of Flocking Strategy-based ghost controllers. Flocking Strategies are a paradigm for intelligent agents characterized by showing emergent behavior and for having very little computational and memory requirements, making them well suited for commercial applications and mobile devices. In particular, we study empirically the effect of optimizing homogeneous and heterogeneous teams. The computational analysis shows that the Flocking Strategy-based controllers generated by the proposed GALR outperform the ghost controllers included in the competition framework and some of those presented in the literature.

The paper can be found here: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7093170&tag=1

Enjoy it!

(And cite us) :D