Abstract: As of 2020, the international workshop on Procedural Content Generation enters its second decade. The annual workshop, hosted by the international conference on the Foundations of Digital Games, has collected a corpus of 95 papers published in its first 10 years. This paper provides an overview of the workshop’s activities and surveys the prevalent research topics emerging over the years.

Abstract: In this paper we introduce an architecture, an implementation and an evaluation of a system for the automatic creation of interactive stories for games. Our goal is to algorithmically create a branched story for the entire game; in each game run a different variant is generated. The architecture is based on natural language processing (NLP) to generate meaningful stories. For NLP we use a statistical language model based on a neural network (Generative Pretrained Transformer, GPT-2). The basic stories generated in this way tend to have too many different persons, and they tend to get incoherent for longer texts, so we add a component restricting the number of persons and improving the consistency. The system is initialized with a hand-written game introduction that defines the main characters and the inventory; it also sets the goals for the game. From that text the remainder of the game story is generated algorithmically. We have fully implemented our system, and we report initial, encouraging experimental results.

Abstract: Quantum computation is an emerging technology that promises to be a powerful tool in many areas. Though some years likely still remain until significant quantum advantage is demonstrated, the development of the technology has led to a range of valuable resources. These include publicly available prototype quantum hardware, advanced simulators for small quantum programs and programming frameworks to test and develop quantum software. In this provocation paper we seek to demonstrate that these resources are sufficient to provide the first useful results in the field of procedural generation. This is done by introducing a proof-of-principle method: a quantum generalization of a blurring process, in which quantum interference is used to provide a unique effect. Through this we hope to show that further developments in the technology are not required before it becomes useful for procedural generation. Rather, fruitful experimentation with this new technology can begin now.

Abstract: In this article, we present a preliminary analysis of using parameterized Generative Adversarial Networks (GANs) to produce levels for the puzzle game Lily’s Garden\footnote{https://tactilegames.com/lilys-garden/}, based on an existing body of human-made levels. This could be greatly beneficial in the context of Procedural Content Generation (PCG) and possibly assist level-designers create new levels more efficiently.

As Lily’s Garden has many different unique board pieces (henceforth pieces) and further combinations of those pieces, we reduce the data for the levels to a representation with 8 unique pieces and extract two condition-vectors from the real levels in an effort to give a potential designer control over the output of the GANs. The conditions are level-shape and piece-distribution.

The GANs successfully approximates the level shape given to it, even on shapes that it has not been trained on, but fails to approximate the piece-distribution vector. The GANs are also evaluated on two tests, more specific to the puzzle-game genre: testing for color-islands (pieces with the same color are connected, making them clickable), and testing for broken pieces (which means that there are color- or blocker pieces outside of the level shape).

While the GANs performs well in the first test it struggles greatly with the latter and we suggest that this might be improved by trying out alternative architectures for both the Generator and Discriminator of the GANs. Further, we hypothesize that the GANs might better pick up on the piece-distribution vector if we enrich the dataset with more channels than the current reduced problem.

Abstract: Extensive research has been conducted on using procedural music generation in real-time applications such as accompaniment to musicians, visual narratives, and games. However, less attention has been paid to the enhancement of textual narratives through music. In this paper, we present Mood Into Note Using Extracted Text (MINUET), a novel system that can procedurally generate music for textual narrative segments using sentiment analysis. Textual analysis of the flow and sentiment derived from the text is used as input to condition accompanying music. Music generation systems have addressed variations through changes in sentiment. By using an ensemble predictor model to classify sentences as belonging to particular emotions, MINUET generates text-accompanying music with the goal of enhancing a reader’s experience beyond the limits of the author’s words. Music is played via the JMusic library and a set of Markov chains specific to each emotion with mood classifications evaluated via stratified 10-fold cross validation. The development of MINUET affords the reflection and analysis of features that affect the quality of generated musical accompaniment for text. It also serves as a sandbox for further evaluating sentiment-based systems on both text and music generation sides in a coherent experience of an implemented and extendable experiential artifact.

Abstract: Dungeon generation is among the oldest problems in procedural content generation. Creating the spatial aspects of a dungeon requires three steps: random generation of rooms and sizes, placement of these rooms inside a fixed area, and connecting rooms with passageways. This paper uses a series of integer linear constraints, solved by an SMT solver, to perform the placement step. Separation constraints ensure dungeon rooms do not intersect and maintain a minimum fixed separation. Designers can specify control lines, and dungeon rooms will be placed within a fixed distance of these control lines. Generation times vary with number of rooms and constraints, but are often very fast. Spatial distribution of solutions tend to have hot spots, but is surprisingly uniform given the underlying complexity of the solver. The approach demonstrates the effectiveness of a declarative approach to dungeon layout generation, where designers can express desired intent, and the SMT solver satisfies this if possible.

Abstract: Existing methods of level generation using latent variable models such as VAEs and GANs do so in segments and produce the final level by stitching these separately generated segments together. In this paper, we augment such methods by training VAEs to learn a sequential model of segment generation such that generated segments logically follow from prior segments. By further combining the VAE with a classifier that determines whether to place the generated segment to the top, bottom, left or right of the previous segment, we obtain a pipeline that enables the generation of arbitrarily long levels that progress in any of these four directions and are composed of segments that logically follow one another. In addition to generating more coherent levels of non-fixed length, this method also enables implicit blending of levels from separate games that do not have similar orientation. We demonstrate our approach using levels from Super Mario Bros., Kid Icarus and Mega Man, showing that our method produces levels that are more coherent than previous latent variable-based approaches and are capable of blending levels across games.

Abstract: Tabletop roleplaying games (TTRPGs) and procedural content generators can both be understood as systems of rules for producing content. In this paper, we argue that TTRPG design can usefully be viewed as procedural content generator design. We present several case studies linking key concepts from PCG research — including possibility spaces, expressive range analysis, and generative pipelines — to key concepts in TTRPG design. We then discuss the implications of these relationships and suggest directions for future work uniting research in TTRPGs and PCG.

Abstract: Procedural level generation is a hard problem. Game designers usually design their constructive level generators using game knowledge. This paper introduces a new system to design constructive level generators by searching the space of constructive level generators defined by Marahel language. We use NSGA-II, a multi-objective optimization algorithm, to search for generators for three different problems (Binary, Zelda, and Sokoban). We restrict the representation to a subset of Marahel language to push the evolution to find more efficient generators. The results show that the generated generators were able to achieve a good performance on most of the fitness function over these three problems.