En la Sesión sobre “Videojuegos e Inteligencia Computacional”, celebrada recientemente en MAEB 2010, Valencia, presentamos la versión de lanzamiento del Videojuego Chapas, que utiliza la tecnología Genetic Terrain Programming para la generación de terrenos.
El proyecto en software libre tiene su forja accesible en: https://forja.unex.es/projects/chapas
Incluímos un vídeo del juego. Disfrútalo.
Presentación del paper “Evolución de FFSM para el control de bots en juegos FPS” en el MAEB 2010
Not only technology is required for living in balance. This year, besides presenting latest results on Automation Software Testing at Evo* Conference, we have also presented some kind of art for videogames.
New results on Automatic Genetic Terrain Generation were presented at EvoGames 2010, as well as this video on Chapas, the videogame using the technology developed.
Let us know of you want to become beta-tester for Chapas.
Here you are the AMAZING (:D) poster presented at EVO* 2010, inside the Workshop EVOGames.
The work describes the analysis, design and implementation of two evolutionary algorithms (a Genetic Algorithm and a Genetic Programming one) to improve the default Artificial Intelligence of the Bots (authonomous enemies) inside the PC Game Unreal.
It had plenty of visits and millards of interest people ask me about the topics in it…
… well, to be honest just, 10 people ask me (3 of them are friends of mine), but in some moments, around 8 or 10 people were looking at the poster and making jokes with me, since it seems to be a perfect mixture between a fabulous research study (:D) and a funny PC game.😉
On November 5 and 6 the Instituto Tecnologico de Informatica, Universidad Politecnica de Valencia, Spain organises JICAN 2009, the Workshop on Computational Intelligence Applied to Business.
This is a unique 2-day workshop exclusively devoted to real-world applications of CI, with the support of the Spanish Chapter of IEEE-Computational Intelligence Society.
The purpose of the workshop is twofold: on the one hand, to introduce this technology to companies in any area; on the other, to show actual fields of application to researchers in academia.
We are privileged to count on the participation of world leading experts in Computational Intelligence, who will present the latest developments in CI with a special focus on how it is used in real world business and industry applications (specifically in videogames, logistics, social networks, planning and scheduling…)..
The workshop is aimed at:
- Companies: Project managers or directors (technical and non technical) who, as part of their work, face complex problems related to optimisation, prediction and decision making.
- Academia: Researchers and students interested in the practical application of Computational Intelligence.
The languages of the Workshop are English and Spanish. Two-way simultaneous translation will be provided to those who require it.
See full programme here
Our paper “Multiobjective Genetic Programming Approach for a Smooth Modeling of the Release Kinetics of a Pheromone Dispenser” was presented at the workshop on Symbolic Regression and Modeling, part of the Genetic and Evolutionary Computation Conference, GECCO, held in Montreal, Canada, from July 8th ot 12th, 2009.
Here’s the abstract:
The accurate modeling of the release kinetics of pheromone dispensers is a matter or great importance for ensuring that the dispenser field-life covers the right period of the pest and for optimizing the layout of dispensers in the treated area.
A new experimental dispenser has been recently designed by researchers at the Instituto Agroforestal del Mediterraneo – Centro de Ecologia Quimica Agricola (CEQA) of the Universidad Politecnica de Valencia (Spain). The most challenging problem for the modeling of the release kinetics of this dispensers is the dificulty in obtaining experimental measurements for building the model. The procedure for obtaining these data is very costly, both time and money wise, therefore the available data across the whole season are scarce. In prior work we demonstrated the utility of using Genetic Programming (GP) for this particular problem. However, the models evolved by the GP algorithm tend to have discontinuities in those time ranges where there are not available measurements. In this work we propose the use of a multiobjective Genetic Programming for modeling the performance of the CEQA dispenser. We take two approaches, involving two and nine objectives respectively. In the first one, one of the objectives of the GP algorithm deals with how well the model fits the experimental data, while the second objective measures how “smooth” the model behaviour is. In the second approach we have as many objectives as data points and the aim is to predict each point separately using the remaining ones. The results obtained endorse the utility of this approach for those modeling problems characterized by the lack of experimental data.
El Jueves 21 de Mayo de 2009 se celebró el seminario “La IA en la adquisición y uso del conocimiento médico: Patrones Asistenciales” en la ETSI Informática de la UNED. El seminario presentaba el proyecto HYGIA (TIN2006-15453), el cual integra el trabajo conjunto desarrollado por las universidades de Santiago de Compostela, Jaume I y Rovira i Virgili, y del Hospital Clínico de Barcelona en el ámbito de la utilización de técnicas de análisis automático de textos, ingeniería del conocimiento y aprendizaje automático inductivo para la generación de patrones asistenciales. Estos patrones formalizan el conocimiento terapéutico de una o varias enfermedades y son capaces de ser interpretados por sistemas informáticos de soporte a la toma de decisiones en medicina. En esta ponencia se presentó el trabajo y los resultados obtenidos en el análisis automático de textos médicos y en el aprendizaje inductivo de patrones a partir de las bases de datos hospitalarias.
La web oficial del proyecto es: http://banzai-deim.urv.net/~riano/TIN2006-15453/
Our work on modeling pheromone dispensers for sexual confusion in agricluture was presented by Anna Esparcia in the 2nd European workshop on Bio-inspired algorithms for continuous parameter optimisation, EvoNUM 2009, which is part of EvoStar 2009, the premier European event on Evolutionary Computation.
Although the presentation was scheduled at the ungodly hour of 10:30, the comments were higly favourable. As in the previous occasion where we have presented this work, the application was received with great interest, partly because computer scientists are in general unaware of where their food comes from.
However, unlike in MAEB 2009, this time we did not get what has become a memorable comment: “Us people from Madrid only go to the country to check that cows are in fact not purple”.
In such crisis times as these, the recently released chapter “Finding relevant variables in a financial distress prediction problem using Genetic Programming and Self-Organizing Maps” from volume 2 of “Natural Computing in Computational Finance” can come in quite handy.
In it we use Genetic Programming to generate models of prediction of book loses in companies, and we tackle the problem of finding the relevant variables for the prediction using Genetic Programming and Kohonen’s Self Organising Maps. This approach singificantly reduces the number of variables used for the analysis, while minimising the prediction error.
In other words, we tell you what you have to look at if you want to avoid losing money in your company – but don’t hold us responsible if you don’t like the results!
The chapter can also be obtained from SpringerLink.
You should also check our financial prediction tool PRESAGI