El pasado viernes (27 de Julio), presenté una charla dentro del curso Animación y Videojuegos, ofrecido por el Centro Mediterráneo de Almuñécar.
En ella comenté en tono desenfadado las relaciones existentes entre ambos mundos, considerando tanto las aportaciones de los sistemas de videojuegos al entorno científico, como los avances en investigación dentro del campo de los videojuegos.
Podéis encontrarla en Slideshare y aquí mismo (:D):
Que la disfrutéis. ;)
Además, nos hicieron una ‘super-entrevista’ mientras hacíamos networking (:P) y la publicaron en la edición online de Ideal Costa:
Last Wednesday, my latest swarm-art project “Abstracting the Abstract” (with the collaboration of JJ Merelo and Antonio Mora) won the GECCO Evolutionary Art, Design and Creativity Competition. The work is based on KANTS, a ant algorithm for clustering and classification that was proposed by our research group in 2008. Early this year, I started using KANTS as a generative art tool, by trying to generate 2-dimensional representations of human sleep. The first results will be presented in October in the ECTA2012. “Abstracting the Abstract” goes one step further and uses famous abstract paintings as input samples to the KANTS algorithm. The result is the swarm’s “interpretation” of the painting. Here and here you’ll find, respectively, the artist statement and the technical paper. Below you’ll find an example of a swarm painting, as well as GECCO’s presentation.
Carlos M. Fernandes, Abstracting the Abstract #5 (after Miró)
Since nobody is reviewing this, I’m not giving a long answer this question posed by one of the reviewers of a paper I submitted ages ago, in the nineties, proposing solutionsusing evolutionary algorithms to the game. However, the short question, as happens with most everything in Science, it’s because it’s there. However, after this empirical study of exhaustive solutions it is a bit closer.
In this paper, which is a draft of something we intend to submit to a journal in the near future, we describe something that has been disregarded in Mastermind solutions: the pure chance of drawing the correct solution in the opening moves. In fact, this chance dominates in the first moves, until the search space is reduced to a single solution, which is usually the intention of most empirical solutions to the game.
Which means that a method that is able to increase that chance, will be able to beat traditional solutions. In fact, it does not, but it is consistently as good as the best solution for each size.
It is rather a longish paper (and might become even more so before submission), but you might learn a thing or two about Mastermind. Besides, it is intended as a base for future papers that will apply our usual techniques, evolutionary algorithms.