Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not require hand authoring of initial content or rules. Instead, PCGML relies on existing content and black box models, which can be difficult to tune or tweak without expert knowledge. This is especially problematic when a human designer needs to understand how to manipulate their data or models to achieve desired results. We present an approach to Explainable PCGML via Design Patterns in which the design patterns act as a vocabulary and mode of interaction between user and model. We demonstrate that our technique outperforms non-explainable versions of our system in interactions with five expert designers, four of whom lack any machine learning expertise.
Design patterns represent a well-researched approach to game design @cite_3 . In theory, game design patterns describe general solutions to game design problems that occur across many different games. Game Design patterns have been used as heuristics in evolutionary PCG systems including in the domain of Super Mario Bros. @cite_24 . Researchers tend to derive game design patterns through either rigorous, cross-domain analysis @cite_21 or based upon their subjective interpretation of game structure. We embrace this subjectivity in our work by having designers create a language of game design patterns unique to themselves with which to interact with a PCGML system.
@cite_24 @cite_21 @cite_3
2027451374 283812555 2700693968 1989846335
Procedural content generation and design patterns could potentially be combined in several different ways in game design. This paper discusses how to combine the two, using automatic platform game level design as an example. The paper also present work towards a pattern-based level generator for Super Mario Bros. (SMB), which is based on an analysis of the levels of the original SMB game where we found 23 different patterns. PART I BACKGROUND 1 Introduction 2 An Activity-Based Framework for Describing Games 3 Game Design Patterns PART II THE PATTERN 4 Using Design Patterns 5 Game Design Patterns for Game Elements 6 Game Design Patterns for Resource and Resource Management 7 Game Design Patterns for Information, Communication, and Presentation 8 Actions and Events Patterns 9 Game Design Patterns for Narrative Structures, Predictability, and Immersion Patterns 10 Game Design Patterns for Social Interaction 11 Game Design Patterns for Goals 12 Game Design Patterns for Goal Structures 13 Game Design Patterns for Game Sessions 14 Game Design Patterns for Game Mastery and Balancing 15 Game Design Patterns for Meta Games, Replayability, and Learning Curves Procedural Content Generation (PCG) can be a useful tool for aiding creativity in the process of designing game levels. Mixed-initiative level generation tools where a designer and an algorithm collaborate to iteratively generate game levels have been used for this purpose. However, it can be difficult for designers to work with tools that do not respond to the common language of games: game design patterns. We present the Evolutionary Dungeon Designer, the first step towards a mixed-initiative dungeon design tool which evolves dungeon rooms using game design patterns, as well as several metrics regarding the placement of treasures and enemies, in the fitness function of a genetic algorithm. Our results show that we are able to control the frequency, shape and type of design patterns, as well as properly place enemies and treasures in the generated rooms, using design pattern-related input parameters. Video games today increasingly situate play in imaginative 3D worlds. As a result, the industry devotes much time and effort to level design. However, this subject has received very little research. Documentation on the process of level design or how designers push or pull players through a level within a video game is very sparse. In this paper, we propose a set of design patterns for level design. The patterns were developed based on a process involving interviews with game designers as well as gameplay analysis of different games. We established face validity of these patterns through expert review; we also established reliability using inter-rater agreement. In addition, we also developed a timeline video annotation method based on these patterns. This visualization method provides a very effective approach to view players' play style and preference as well as level design problems. The patterns as well as the visualization method will be discussed in the paper.