Procedural Content Generation In Games Research Articles

Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games

The term Procedural Content Generation (PCG) refers to the (semi-)automatic generation of game content by algorithmic means, and its methods are becoming increasingly popular in game-oriented research and industry. A special class of these methods, which is commonly known as search-based PCG, treats the given task as an optimisation problem. Such problems are predominantly tackled by evolutionary algorithms.We will demonstrate in this paper that obtaining more information about the defined optimisation problem can substantially improve our understanding of how to approach the generation of content. To do so, we present and discuss three efficient analysis tools, namely diagonal walks, the estimation of high-level properties, as well as problem similarity measures. We discuss the purpose of each of the considered methods in the context of PCG and provide guidelines for the interpretation of the results received. This way we aim to provide methods for the comparison of PCG approaches and eventually, increase the quality and practicality of generated content in industry.

The Future of Procedural Content Generation in Games

The future of procedural content generation (PCG) lies beyond the dominant motivations of “replayability” and creating large environments for players to explore. This paper explores both the past and potential future for PCG, identifying five major lenses through which we can view PCG and its role in a game: data vs. process intensiveness, the interactive extent of the content, who has control over the generator, how many players interact with it, and the aesthetic purpose for PCG being used in the game. Using these lenses, the paper proposes several new research directions for PCG that require both deep technical research and innovative game design.

Co-Generation of Game Levels and Game-Playing Agents

Open-endedness, a longstanding cornerstone of artificial life research, is the ability of systems to generate potentially unbounded ontologies of increasing novelty and complexity. Engineering generative systems displaying at least some degree of this ability is a goal with clear applications to procedural content generation in games. The Paired Open-Ended Trailblazer (POET) algorithm, heretofore explored only in a biped walking domain, is a coevolutionary system that simultaneously generates environments and agents that can solve them. This paper introduces a POET-Inspired Neuroevolutionary System for KreativitY (PINSKY) in games, which co-generates levels for multiple video games and agents that play them. This system leverages the General Video Game Artificial Intelligence (GVGAI) framework to enable co-generation of levels and agents for the 2D Atari-style games Zelda and Solar Fox. Results demonstrate the ability of PINSKY to generate curricula of game levels, opening up a promising new avenue for research at the intersection of procedural content generation and artificial life. At the same time, results in these challenging game domains highlight the limitations of the current algorithm and opportunities for improvement.

PCGRL: Procedural Content Generation via Reinforcement Learning

We investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, where level design is framed as a game, and the content generator itself is learned. By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action so that the expected final level quality is maximized. This approach can be used when few or no examples exist to train from, and the trained generator is very fast. We investigate three different ways of transforming two-dimensional level design problems into Markov decision processes, and apply these to three game environments.

Graph Based Wave Function Collapse Algorithm for Procedural Content Generation in Games

Graph Based Wave Function Collapse Algorithm for Procedural Content Generation in Games

A multi-objective evolutionary approach for the nonlinear scale-free level problem

Procedural Content Generation in games aims to produce replayable levels that might adapt to the player or designer preferences. One of the most popular approaches adopted for search-based level generation is the use of Evolutionary Algorithms. This paper addresses the context of dungeon generation and divide the problem in two steps: the first consists of generating the physical level, while the second is to create the puzzle. Although there are several approaches in the related literature for generating levels and puzzles, few are successful at generating unpredictable or diverse levels. The objective of this work is to propose a Multi-objective Evolutionary approach for generating the level and creating the puzzle providing a wide range of unpredictable and diverse solutions with different level dimensions. To achieve this goal, the level is modeled as scale-free topology with a nonlinear resolution. This model avoids the generation of linear, repetitive and grid-like levels, giving the algorithm additional freedom to explore the search space for diverse solutions. Four classical well-known evolutionary algorithms (SPEA2, PAES, NSGA-II and MOCell) were applied to both sub-problems. To analyze the results, we consider a set of quality indicators covering both convergence and diversity. Our results indicated that, the proposed multi-objective formulation was able to provide a wide range of satisfactory and diverse solutions with different level dimensions, independently of the algorithm used. Thus, the main contribution of this work is that level designers can choose between different solutions, based on solution properties and/or designer priorities.

Procedurally generating a digital math game’s levels: Does it impact players’ in-game behavior?

Developing games can be improved with Procedural Content Generation (PCG), the automatic creation of game contents, such as levels, music, and vegetation. However, few researches have addressed the impacts of PCG on players, especially in terms of games with educational ends. Hence, this understanding is a research area that demands further studies. To expand on this gap, this article presents a study concerning whether PCG for level creation impacts players’ in-game behavior, based on interactions with a digital math game. This game features two versions that contain a unique difference: while one features human-designed levels, these are procedurally generated on the other version. To compare them, PCG’s impact on players’ in-game behavior was measured through the total played time, number of played levels, and number of retained players. In this context, the findings demonstrated a significant difference in the number of retained players, which was higher for the PCG version in comparison to the other. In contrast, the other two metrics were insignificantly different between versions. Therefore, game designers and developers can exploit these findings to employ PCG in games, taking advantage of its impacts on development and players, knowing how it is expected to affect players’ in-game behavior.

Uncertainty Handling in Surrogate Assisted Optimisation of Games

Real-world problems are often affected by uncertainties of different types and from multiple sources. Algorithms created for expensive optimisation, such as model-based optimisers, introduce additional errors. We argue that these uncertainties should be accounted for during the optimisation process. We thus introduce a benchmark as well as a new surrogate-assisted evolutionary algorithm to investigate this hypothesis further. The benchmark includes two function suites based on procedural content generation for games, which is a common problem observed in games research and also mirrors several types of uncertainties in the real-world. We find that observing and handling the uncertainty present in the problem can improve the optimiser, and also provides valuable insight into the function characteristics.

Procedural Content Generation in Games towards Semantic Web

Procedural Content Generation in Games towards Semantic Web

Procedural Content Generation for Game Props? A Study on the Effects on User Experience

This work demonstrates the potentials of procedural content generation (PCG) for games, focusing on the generation of specific graphic props (reefs) in an explorer game. We briefly portray the state-of-the-art of PCG and compare various methods to create random patterns at runtime. Taking a step towards the game industry, we describe an actual game production and provide a detailed pseudocode implementation showing how Perlin or Simplex noise can be used efficiently. In a comparative study, we investigate two alternative implementations of a decisive game prop: once created traditionally by artists and once generated by procedural algorithms. 41 test subjects played both implementations. The analysis shows that PCG can create a user experience that is significantly more realistic and at the same time perceived as more aesthetically pleasing. In addition, the ever-changing nature of the procedurally generated environments is preferred with high significance, especially by players aged 45 and above.

Procedural content generation for games

Hundreds of millions of people play computer games every day. For them, game content—from 3D objects to abstract puzzles—plays a major entertainment role. Manual labor has so far ensured that the quality and quantity of game content matched the demands of the playing community, but is facing new scalability challenges due to the exponential growth over the last decade of both the gamer population and the production costs. Procedural Content Generation for Games (PCG-G) may address these challenges by automating, or aiding in, game content generation. PCG-G is difficult, since the generator has to create the content, satisfy constraints imposed by the artist, and return interesting instances for gamers. Despite a large body of research focusing on PCG-G, particularly over the past decade, ours is the first comprehensive survey of the field of PCG-G. We first introduce a comprehensive, six-layered taxonomy of game content: bits, space, systems, scenarios, design, and derived. Second, we survey the methods used across the whole field of PCG-G from a large research body. Third, we map PCG-G methods to game content layers; it turns out that many of the methods used to generate game content from one layer can be used to generate content from another. We also survey the use of methods in practice, that is, in commercial or prototype games. Fourth and last, we discuss several directions for future research in PCG-G, which we believe deserve close attention in the near future.

A shape grammar approach to computational creativity and procedural content generation in massively multiplayer online role playing games

With rapid growth in both production costs and player populations over the last decade, the computer games industry is facing new scalability challenges in game design and content generation. The application of computers to these tasks – called procedural content generation – has the potential to reduce the time, cost and labor required to produce games. A range of generative algorithms have so far been proposed for procedural content generation. However, automated game design requires not only the ability to generate content, but also the ability to judge and ensure the novelty, quality and cultural value of generated content. This includes factors such as the surprise-value of generated content as well as the usefulness of content in the context of a particular game design. Studies of human designers have identified that the ability to generate artefacts that are novel, surprising, useful and valuable are facets of the human cognitive capacity for creativity. This suggests that computational models of creativity may be an important consideration for developing tools that can aid in or automate design processes. However, such cognitive models have not yet been widely considered for use in procedural content generation for games. This paper presents a framework for procedural content generation systems that use computational models of creativity as a part of the generative process. We demonstrate an example of such a system for generating instances for massively multiplayer, online role-playing games. The system combines the generative shape grammar formalism with a computational model of interest based on the Wundt curve to select new designs that are similar-yet-different to existing human designs. The approach aims to capture the usefulness and value of an existing human design while introducing novel or surprising variations through the model of interest. The system incorporates a metric that permits generated designs to be evaluated in terms of both their similarity to human designs and their novelty in the context of existing designs.

Guest Editorial: Procedural Content Generation in Games

The eight papers in this special issue focus on procedural content generation in games. They present a good combination of surveys, conceptual frameworks, innovative methods, and applications.