69 research outputs found
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
Game Artificial Intelligence: Challenges for the Scientific Community
This paper discusses some of the most interesting challenges to which the games research community members may face in the área of the application of arti cial or computational intelligence techniques to the design and creation of video games. The paper focuses on three lines that certainly will in uence signi cantly the industry of game development in the near future, speci cally on the automatic generation of
content, the a ective computing applied to video games and the generation of behaviors that manage the decisions of entities not controlled by
the human player.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Increasing generality in machine learning through procedural content generation
Procedural Content Generation (PCG) refers to the practice, in videogames and
other games, of generating content such as levels, quests, or characters
algorithmically. Motivated by the need to make games replayable, as well as to
reduce authoring burden, limit storage space requirements, and enable
particular aesthetics, a large number of PCG methods have been devised by game
developers. Additionally, researchers have explored adapting methods from
machine learning, optimization, and constraint solving to PCG problems. Games
have been widely used in AI research since the inception of the field, and in
recent years have been used to develop and benchmark new machine learning
algorithms. Through this practice, it has become more apparent that these
algorithms are susceptible to overfitting. Often, an algorithm will not learn a
general policy, but instead a policy that will only work for a particular
version of a particular task with particular initial parameters. In response,
researchers have begun exploring randomization of problem parameters to
counteract such overfitting and to allow trained policies to more easily
transfer from one environment to another, such as from a simulated robot to a
robot in the real world. Here we review the large amount of existing work on
PCG, which we believe has an important role to play in increasing the
generality of machine learning methods. The main goal here is to present RL/AI
with new tools from the PCG toolbox, and its secondary goal is to explain to
game developers and researchers a way in which their work is relevant to AI
research
Game-based Platforms for Artificial Intelligence Research
Games have been the perfect test-beds for artificial intelligence research
for the characteristics that widely exist in real-world scenarios. Learning and
optimisation, decision making in dynamic and uncertain environments, game
theory, planning and scheduling, design and education are common research areas
shared between games and real-world problems. Numerous open-sourced games or
game-based environments have been implemented for studying artificial
intelligence. In addition to single- or multi-player, collaborative or
adversarial games, there has also been growing interest in implementing
platforms for creative design in recent years. Those platforms provide ideal
benchmarks for exploring and comparing artificial intelligence ideas and
techniques. This paper reviews the game-based platforms for artificial
intelligence research, discusses the research trend induced by the evolution of
those platforms, and gives an outlook
Bootstrapping Conditional GANs for Video Game Level Generation
Generative Adversarial Networks (GANs) have shown im-pressive results for
image generation. However, GANs facechallenges in generating contents with
certain types of con-straints, such as game levels. Specifically, it is
difficult togenerate levels that have aesthetic appeal and are playable atthe
same time. Additionally, because training data usually islimited, it is
challenging to generate unique levels with cur-rent GANs. In this paper, we
propose a new GAN architec-ture namedConditional Embedding Self-Attention
Genera-tive Adversarial Network(CESAGAN) and a new bootstrap-ping training
procedure. The CESAGAN is a modification ofthe self-attention GAN that
incorporates an embedding fea-ture vector input to condition the training of
the discriminatorand generator. This allows the network to model
non-localdependency between game objects, and to count objects. Ad-ditionally,
to reduce the number of levels necessary to trainthe GAN, we propose a
bootstrapping mechanism in whichplayable generated levels are added to the
training set. Theresults demonstrate that the new approach does not only
gen-erate a larger number of levels that are playable but also gen-erates fewer
duplicate levels compared to a standard GAN
Literature review of procedural content generation in puzzle games
This is the third chapter from my Master Thesis (Automatic Game Generation). This
chapter will provide a review of the past work on Procedural Content Generation. It
highlights different efforts towards generating levels and rules for games. These efforts are
grouped according to their similarity and sorted chronologically within each group.N/
From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI
This paper reviews the field of Game AI, which not only deals with creating
agents that can play a certain game, but also with areas as diverse as creating
game content automatically, game analytics, or player modelling. While Game AI
was for a long time not very well recognized by the larger scientific
community, it has established itself as a research area for developing and
testing the most advanced forms of AI algorithms and articles covering advances
in mastering video games such as StarCraft 2 and Quake III appear in the most
prestigious journals. Because of the growth of the field, a single review
cannot cover it completely. Therefore, we put a focus on important recent
developments, including that advances in Game AI are starting to be extended to
areas outside of games, such as robotics or the synthesis of chemicals. In this
article, we review the algorithms and methods that have paved the way for these
breakthroughs, report on the other important areas of Game AI research, and
also point out exciting directions for the future of Game AI
Enhancing automatic level generation for platform videogames
This dissertation addresses the challenge of improving automatic level generation processes for plat-form videogames. As Procedural Content Generation (PCG) techniques evolved from the creation of simple elements to the construction of complete levels and scenarios, the principles behind the generation algorithms became more ambitious and complex, representing features that beforehand were only possible with human design. PCG goes beyond the search for valid geometries that can be used as levels, where multiple challenges are represented in an adequate way. It is also a search for user-centred design content and the creativity sparks of humanly created content.
In order to improve the creativity capabilities of such generation algorithms, we conducted part of our research directed to the creation of new techniques using more ambitious design patterns. For this purpose, we have implemented two overall structure generation algorithms and created an addi-tional adaptation algorithm. The later can transform simple branched paths into more compelling game challenges by adding items and other elements in specific places, such as gates and levers for their activation. Such approach is suitable to avoid excessive level linearity and to represent certain design patterns with additional content richness.
Moreover, content adaptation was transposed from general design domain to user-centred principles. In this particular case, we analysed success and failure patterns in action videogames and proposed a set of metrics to estimate difficulty, taking into account that each user has a different perception of that concept. This type of information serves the generation algorithms to make them more directed to the creation of personalised experiences.
Furthermore, the conducted research also aimed to the integration of different techniques into a common ground. For this purpose, we have developed a general framework to represent content of platform videogames, compatible with several titles within the genre. Our algorithms run over this framework, whereby they are generic and game independent. We defined a modular architecture for the generation process, using this framework to normalise the content that is shared by multiple modules. A level editor tool was also created, which allows human level design and the testing of automatic generation algorithms. An adapted version of the editor was implemented for the semi-automatic creation of levels, in which the designer may simply define the type of content that he/she desires, in the form of quests and missions, and the system creates a corresponding level structure. This materialises our idea of bridging human high-level design patterns with lower level automated generation algorithms.
Finally, we integrated the different contributions into a game prototype. This implementation allowed testing the different proposed approaches altogether, reinforcing the validity of the proposed archi-tecture and framework. It also allowed performing a more complete gameplay data retrieval in order to strengthen and validate the proposed metrics regarding difficulty perceptions
The Art of Adaptation in Film and Video Games
This Special Issue of Arts explores the art and practice of adaptation in several different mediums with a focus on film and video games. The topics covered include experimental game design, narrative design, film and trauma, games adapted from literature, video game cinema, film and the pandemic, film and the environment, film and immigration, and film and culture
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