4,233 research outputs found
Evolving Aesthetic Maps for a Real Time Strategy Game
Artículo publicado en congreso SEED'2013 (I Spanish Symposium on Entertainment Computing), Septiembre 2013, Madrid.This paper presents a procedural content generator method that have
been able to generate aesthetic maps for a real-time strategy game. The
maps has been characterized based on several of their properties in order
to de ne a similarity function between scenarios. This function has guided
a multi-objective evolution strategy during the process of generating and
evolving scenarios that are similar to other aesthetic maps while being
di erent to a set of non-aesthetic scenarios. The solutions have been
checked using a support-vector machine classi er and a self-organizing
map obtaining successful results (generated maps have been classi ed as
aesthetic maps)
A GPU-based Evolution Strategy for Optic Disk Detection in Retinal Images
La ejecución paralela de aplicaciones usando unidades de procesamiento gráfico (gpu) ha ganado gran interés en la comunidad académica en los años recientes. La computación paralela puede ser aplicada a las estrategias evolutivas para procesar individuos dentro de una población, sin embargo, las estrategias evolutivas se caracterizan por un significativo consumo de recursos computacionales al resolver problemas de gran tamaño o aquellos que se modelan mediante funciones de aptitud complejas. Este artículo describe la implementación de una estrategia evolutiva para la detección del disco óptico en imágenes de retina usando Compute Unified Device Architecture (cuda). Los resultados experimentales muestran que el tiempo de ejecución para la detección del disco óptico logra una aceleración de 5 a 7 veces, comparado con la ejecución secuencial en una cpu convencional.Parallel processing using graphic processing units (GPUs) has attracted much research interest in recent years. Parallel computation can be applied to evolution strategy (ES) for processing individuals in a population, but evolutionary strategies are time consuming to solve large computational problems or complex fitness functions. In this paper we describe the implementation of an improved ES for optic disk detection in retinal images using the Compute Unified Device Architecture (CUDA) environment. In the experimental results we show that the computational time for optic disk detection task has a speedup factor of 5x and 7x compared to an implementation on a mainstream CPU
Tile Pattern KL-Divergence for Analysing and Evolving Game Levels
This paper provides a detailed investigation of using the Kullback-Leibler
(KL) Divergence as a way to compare and analyse game-levels, and hence to use
the measure as the objective function of an evolutionary algorithm to evolve
new levels. We describe the benefits of its asymmetry for level analysis and
demonstrate how (not surprisingly) the quality of the results depends on the
features used. Here we use tile-patterns of various sizes as features.
When using the measure for evolution-based level generation, we demonstrate
that the choice of variation operator is critical in order to provide an
efficient search process, and introduce a novel convolutional mutation operator
to facilitate this. We compare the results with alternative generators,
including evolving in the latent space of generative adversarial networks, and
Wave Function Collapse. The results clearly show the proposed method to provide
competitive performance, providing reasonable quality results with very fast
training and reasonably fast generation.Comment: 8 pages plus references. Proceedings of GECCO 201
An experimental comparative study for interactive evolutionary computation problems
Proceeding of: EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC, Budapest, Hungary, April 10-12, 2006This paper presents an objective experimental comparative study between four algorithms: the Genetic Algorithm, the Fitness Prediction Genetic Algorithm, the Population Based Incremental Learning algorithm and the purposed method based on the Chromosome Appearance Probability Matrix. The comparative is done with a non subjective evaluation function. The main objective is to validate the efficiency of several methods in Interactive Evolutionary Computation environments. The most important constraint of working within those environments is the user interaction, which affects the results adding time restrictions for the experimentation stage and subjectivity to the validation. The experiments done in this paper replace user interaction with several approaches avoiding user limitations. So far, the results show the efficiency of the purposed algorithm in terms of quality of solutions and convergence speed, two known keys to decrease the user fatigue.This article has been financed by the Spanish founded research MCyT project OPLINK, Ref: TIN2006-08818-C04-02
Ludii -- The Ludemic General Game System
While current General Game Playing (GGP) systems facilitate useful research
in Artificial Intelligence (AI) for game-playing, they are often somewhat
specialised and computationally inefficient. In this paper, we describe the
"ludemic" general game system Ludii, which has the potential to provide an
efficient tool for AI researchers as well as game designers, historians,
educators and practitioners in related fields. Ludii defines games as
structures of ludemes -- high-level, easily understandable game concepts --
which allows for concise and human-understandable game descriptions. We
formally describe Ludii and outline its main benefits: generality,
extensibility, understandability and efficiency. Experimentally, Ludii
outperforms one of the most efficient Game Description Language (GDL)
reasoners, based on a propositional network, in all games available in the
Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of
performance with the more recently proposed Regular Boardgames (RBG) system,
and has various advantages in qualitative aspects such as generality.Comment: Accepted at ECAI 202
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