Procedural Content Generation for Real-Time Strategy Games

Videogames are one of the most important and profitable sectors in the industry of entertainment. Nowadays, the creation of a videogame is often a large-scale endeavor and bears many similarities with, e.g., movie production. On the central tasks in the development of a videogame is content generation, namely the definition of maps, terrains, non-player characters (NPCs) and other graphical, musical and AI-related components of the game. Such generation is costly due to its complexity, the great amount of work required and the need of specialized manpower. Hence the relevance of optimizing the process and alleviating costs. In this sense, procedural content generation (PCG) comes in handy as a means of reducing costs by using algorithmic techniques to automatically generate some game contents. PCG also provides advantages in terms of player experience since the contents generated are typically not fixed but can vary in different playing sessions, and can even adapt to the player herself. For this purpose, the underlying algorithmic technique used for PCG must be also flexible and adaptable. This is the case of computational intelligence in general and evolutionary algorithms in particular. In this work we shall provide an overview of the use of evolutionary intelligence for PCG, with special emphasis on its use within the context of real-time strategy games. We shall show how these techniques can address both playability and aesthetics, as well as improving the game AI

A Self-Adaptive Evolutionary Approach to the Evolution of Aesthetic Maps for a RTS Game

Procedural content generation (PCG) is a research eld on the rise,with numerous papers devoted to this topic. This paper presents a PCG method based on a self-adaptive evolution strategy for the automatic generation of maps for the real-time strategy (RTS) game PlanetWars. These maps are generated in order to ful ll the aesthetic preferences of the user, as implied by her assessment of a collection of maps used as training set. A topological approach is used for the characterization of the maps and their subsequent evaluation: the sphere-of-in uence graph (SIG) of each map is built, several graph-theoretic measures are computed on it, and a feature selection method is utilized to determine adequate subsets of measures to capture the class of the map. A multiobjective evolutionary algorithm is subsequently employed to evolve maps, using these feature sets in order to measure distance to good (aesthetic) and bad (non-aesthetic) maps in the training set. The so-obtained results are visually analyzed and compared to the target maps using a Kohonen network.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Multiplier: Real-Time Strategy Unit Balancing Tool

We have built an application that integrates a technical editor feature and a custom real-time strategy game. The end users are able to use the technical editor feature for tweaking and customizing the unit attributes and progressions in the game using simple mathematical formulas, and they can play or test their tweaked formulas within the game. Various game modes in the software, which are Single Player, Multiplayer, and Simulation, can help display to the end users the results of their tweaked formulas, or users can just have fun by playing the game. The software was evaluated to see whether the software with the editor feature enabled is more attractive and appealing to the end users than the software with the editor feature disabled. The evaluation is based on the players’ feedback on the game with or without the editor. A total of 50 testers were randomly assigned into 2 groups evenly, the Tool group and the Game group. Testers assigned to the Tool group were able to customize the game unit attributes via the editor and play, while the testers in the Game group only play the game. The results from the post-test survey show both versions of the software are highly appealing to the testers, and there is no significant difference in game appeals between the Tool version and the Game version

A spatially-structured PCG method for content diversity in a Physics-based simulation game

This paper presents a spatially-structured evolutionary algorithm (EA) to procedurally generate game maps of di ferent levels of di ficulty to be solved, in Gravityvolve!, a physics-based simulation videogame that we have implemented and which is inspired by the n- body problem, a classical problem in the fi eld of physics and mathematics. The proposal consists of a steady-state EA whose population is partitioned into three groups according to the di ficulty of the generated content (hard, medium or easy) which can be easily adapted to handle the automatic creation of content of diverse nature in other games. In addition, we present three fitness functions, based on multiple criteria (i.e:, intersections, gravitational acceleration and simulations), that were used experimentally to conduct the search process for creating a database of maps with di ferent di ficulty in Gravityvolve!.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Learning the patterns of balance in a multi-player shooter game

A particular challenge of the game design process is when the designer is requested to orchestrate dissimilar elements of games such as visuals, audio, narrative and rules to achieve a specic play experience. Within the domain of adversarial rst person shooter games, for instance, a designer must be able to comprehend the dierences between the weapons available in the game, and appropriately cra a game level to take advantage of strengths and weaknesses of those weapons. As an initial study towards computationally orchestrating dissimilar content generators in games, this paper presents a computational model which can classify a matchup of a team-based shooter game as balanced or as favoring one or the other team. e computational model uses convolutional neural networks to learn how game balance is aected by the level, represented as an image, and each team’s weapon parameters. e model was trained on a corpus of over 50,000 simulated games with articial agents on a diverse set of levels created by 39 dierent generators. e results show that the fusion of levels, when processed by a convolutional neural network, and weapon parameters yields an accuracy far above the baseline but also improves accuracy compared to articial neural networks or models which use partial information, such as only the weapon or only the level as input.peer-reviewe

Optimizing Game Performance with Dynamic Level of Detail Mesh Terrain Based on CPU Usage

In making a game, a map is an important component. In making maps, several techniques can be used, one of which uses the Procedural Content Generator (PCG) method. In making maps using PCG can apply the Perlin Noise algorithm, as a generator engine for making maps automatically. The Algorithm Perlin Noise can make a noise gradient of and store values from 1 to 0 in each pixel. This value can be utilized as the height value of a 3D map formed from a point which is then connected to a surface called a mesh. The bigger the mesh, the more detailed a map will be. However, there are obstacles in its formation, namely the burden of the processor in processing the map. The level of detail (LOD) in a mesh will affect the workload of the processor, so we need a dynamic LOD. In this study, game performance measurements were performed using the average FPS with the application of Dynamic LOD and LOD Statistics. The performance test managed to increase processor performance to the maximum extent but did not reduce the overall performance of the game. In table 3 the connectedness calculation uses the person correlation method that the connectedness between the CPU and Vertex has a value of -0.81942 which means that if the CPU goes up the vertices go down, and the value of the connectedness between the CPU and LOD is 0.92299 which means if the CPU performance goes up, the LOD will go up, this indicates The CPU is optimized to run the rendering process and can optimize processor performance

Exploring the visual styles of arcade game assets

This paper describes a method for evolving assets for video games based on their visuals properties. Focusing on assets for a space shooter game, a genotype consisting of turtle commands is transformed into a spaceship image composed of human-authored sprite components. Due to constraints on the final spaceships’ plausibility, the paper investigates two-population constrained optimization and constrained novelty search methods. A sample of visual styles is tested, each a combination of visual metrics which primarily evaluate balance and shape complexity. Experiments with constrained optimization of a visual style demonstrate that a visually consistent set of spaceships can be generated, while experiments with constrained novelty search demonstrate that several distinct visual styles can be discovered by exploring along select, or all, visual dimensions.peer-reviewe

Generación Automática de Contenido para Juegos de Estrategia en Tiempo Real

El destacado volumen de negocio a nivel mundial ha convertido a los videojuegos en el principal protagonista dentro de la industria del entretenimiento. Este hecho no ha pasado desapercibido para la comunidad científica, que se ha visto surtida de nuevos retos y desafíos que abordar. La generación automática de contenido son un conjunto de técnicas que permiten generar de forma algorítmica contenido específico para videojuegos. Estas técnicas reducen costes de producción y diseño, sirven como fuente de creatividad e inspiración para el diseñador, ahorran recursos computacionales a la hora de ejecutar el juego y permiten adaptar el juego al jugador para que la experiencia de juego sea única y personalizada. Generalmente el criterio usado para la generación es el de la jugabilidad, es decir, que el contenido cumpla con las reglas del juego. Esta tesis define una metodología para la generación automática de contenido para juegos de estrategia en tiempo real en base a unos criterios que van más allá de que el contenido sea válido. Esta metodología propone el uso de algoritmos evolutivos para la generación del contenido siguiendo un esquema de generación y prueba. La primera característica exigible al contenido generado que se ha estudiado ha sido el equilibrio entre jugadores. También se ha descrito un método de generación enfocado en esta característica que es capaz de crear mapas que no proporcionan ventaja alguna a los jugadores, independientemente de su habilidad o estrategia elegida. Otra característica deseable que evita que las partidas se conviertan en algo aburrido y monótono y que también ha sido estudiada es el dinamismo. A la hora de buscar el dinamismo se ha optado por dos enfoques distintos, uno basado en los recursos que mantienen los jugadores a lo largo de la partida y otro basado en las confrontaciones entre ellos, que se centra en las batallas y el nivel de naves perdidas. Se han analizado ambas características en conjunto, detectándose que la definición de dinamismo que se ha usado como objetivo de la optimización incluye implícitamente una componente de equilibrio, concluyéndose por tanto que el equilibrio es compatible con un nivel medio de dinamismo. Además, se ha estudiado un enfoque multi-objetivo del problema que ha sacado a la luz otras relaciones entre equilibrio y dinamismo y mediante el cual se ha establecido que conforme aumenta el equilibrio se produce un leve descenso en el dinamismo, acabando con un descenso brusco en la frontera superior del equilibrio. Al igual que con el equilibrio y el dinamismo, se ha abordado el problema de mejorar la estética de los mapas siguiendo dos enfoques, uno geométrico que se basan en la geométrica espacial del mapa (coordinadas y distancias), y otro topológico que se basan en propiedades cualitativas de los mapas que no se ven afectadas por transformaciones geométricas simples y que han sido obtenidas a partir del grafo de esferas de influencia. Los resultados indican que existe una relación lineal entre la diferencia con los mapas estéticos y con los que no lo son, lo que da una idea de la densidad del espacio de búsqueda. Con respecto a las medidas utilizadas, se observa que hay algunas de ellas que tienen una mayor influencia sobre el valor de aptitud que el resto de variables. Por último, tras realizar un análisis cruzado de las soluciones obtenidas con ambos enfoques se observa que ambos enfoques son capaces de generar mapas adecuados, aunque hay que destacar que existe una mayor diversidad en las soluciones del enfoque geométrico que en las del topológico, además de que las soluciones geométricas obtenidas quedan más cerca del conjunto de mapas estéticos que las propias soluciones topológicas según la medida de aptitud de éstas últimas