Evolving missions to create game spaces

This paper describes a search-based generative method which creates game levels by evolving the intended sequence of player actions rather than their spatial layout. The proposed approach evolves graphs where nodes representing player actions are linked to form one or more ways in which a mission can be completed. Initially simple graphs containing the mission’s starting and ending nodes are evolved via mutation operators which expand and prune the graph topology. Evolution is guided by several objective functions which capture game design patterns such as exploration or balance; experiments in this paper explore how these objective functions and their combinations affect the quality and diversity of the evolved mission graphs.peer-reviewe

Controlling Randomness: Using Procedural Generation to Influence Player Uncertainty in Video Games

As video games increase in complexity and length, the use of automatic, or procedural, content generation has become a popular way to reduce the stress on game designers. However, the usage of procedural generation has certain consequences; in many instances, what the computer generates is uncertain to the designer. The intent of this thesis is to demonstrate how procedural generation can be used to intentionally affect the embedded randomness of a game system, enabling game designers to influence the level of uncertainty a player experiences in a nuanced way. This control affords game designers direct control over complex problems like dynamic difficulty adjustment, pacing, or accessibility. Game design will be examined from the perspective of uncertainty and how procedural generation can be used to intentionally add or reduce uncertainty. Various procedural generation techniques will be discussed alongside the types of uncertainty, using case studies to demonstrate the principles in action

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/

Automatic level generation for platform videogames using genetic algorithms

In this document we present an investigation on automatically generating levels for platform videogames. Common approaches for this problem are rhythm based, where input patterns are transformed in a valid geometry, and chunk based, where samples are humanly created and automatically assembled like a puzzle. The proposal hereby presented is to explore this challenge with the usage of Genetic Algorithms, facing it as a search problem, in order to achieve higher expressivity and less linearity than in rhythm based approach and without requiring human creation as it happens with the chunk based approach. With simple heuristics the system is able to generate playable levels in a small amount of time (one level is created in less than a minute) and with considerable diversity, as our results show

Towards procedural map and character generation for the MOBA Game Genre

In this paper, we present an approach to create assets using procedural algorithms in maps generation and dynamic adaptation of characters for a MOBA video game, preserving the balancing feature to players -- Maps are created based on offering equal chances of winning or losing for both teams -- Also, a character adaptation system is developed which allows changing the attributes of players in real-time according to their behaviour, always maintaining the game balanced -- Our tests show the effectiveness of the proposed algorithms to establish the adequate values in a MOBA video gam

Hierarchical Style Modeling: A generative framework for Style-Centric Generation of 3D Models

This work focuses on incorporating style - specifically visual style - into procedural content generation processes. Specifically, I model style as a series of constraints that must be satisfied while an object is generated

Co-operative coevolution for computational creativity: a case study In videogame design

The term procedural content generation (PCG) refers to writing software which can synthesise content for a game (or other media such as film) without further intervention from a designer. PCG has become a rich area of research in recent years, finding new ways to apply artificial intelligence to generate high-quality game content such as levels, weapons or puzzles for games. Such research is generally constrained to a single type of content, however, with the assumption that the remainder of the game's design will be fixed by an external designer. Generating many aspects of a game's design simultaneously, perhaps ultimately generating the entirety of a game's design, using PCG is not a well-explored idea. The notion of automated game design is not well-established, and is not seen as a task distinct from simply performing lots of PCG tasks at the same time. In particular, the high-level design tasks guiding the creative direction of a game are all but completely absent in PCG literature, because it is rare that a designer wishes to hand over such responsibility to a PCG system. We present here ANGELINA, an automated game designer that has developed games using a multi-faceted approach to content generation underpinned by a co-operative co-evolutionary approach which breaks down a game design into several distinct tasks, each of which controlled by an evolutionary subsystem within ANGELINA. We will show that this approach works well to automate game design, can be ported across many game engines and game genres, and can be enhanced and extended using novel computational creativity techniques to give the system a heightened sense of autonomy and independence.Open Acces

Gaze-directed gameplay in first person computer games

The use of eye tracking systems in computer games is still at an early stage. Commercial eye trackers and researches have been focusing in gaze-oriented gameplay as an alternative to traditional input devices. This dissertation proposes to investigate the advantages and disadvantages of the use of these systems in computer games. For it, instead of using eye tracking as a simple direct control input, it is proposed to use it in order to control the attention of the player’s avatar (e.g., if the player notices an obstacle in the way, the avatar will notice it too and avoid it) and the game’s procedural content generation (e.g., spawn obstacles in the opposite side of the screen to where the player’s attention is focused). To demonstrate the value of this proposal, it was developed and is herein presented the first-person shooter "Zombie Runner". Tests showed that the implementation meets the stipulated technical requirements and that, although it still needs improvements in terms of precision and robustness, eye tracking technology can be successfully used to to make the player experience more immersive and challenging.A utilização de sistemas de rastreamento ocular em jogos de computador ainda se encontra numa fase embrionária. Aparelhos de rastreamento ocular comerciais e pesquisas na área têm-se focado em jogabilidade à base da atenção visual como uma alternativa a métodos de entrada tradicionais. Esta dissertação propõe-se a investigar as vantages e desvantagens do uso destes sistemas em jogos de computador. Para isso, invés de se usar rastreamento ocular apenas como um método directo de entrada, é proposto usá-lo para controlar a atenção do personagem do jogo (e.g., se o jogador reparar num obstáculo, a personagem também repara e desvia-se do mesmo) assim como afectar a geração procedimental do jogo (e.g., gerar obstáculos no lado oposto ao qual o jogador tem a sua atenção focada). Para demonstrar o valor desta proposta, foi desenvolvido e aqui apresentado o jogo de tiros em primeira pessoa "Zombie Runner". Os testes demonstraram que a implementação cumpre os requisitos técnicos estipulados e que, apesar de ainda carecer de melhorias em termos de precisão e robustez, a tecnologia para rastreamento ocular pode ser utilizada com sucesso para tornar a experiência do jogador mais imersiva e desafiante

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