Steve Dahlskog

Patterns and procedural content generation: revisiting Mario in world 1 level 1

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.

Linear levels through n-grams

We show that novel, linear game levels can be created using n-grams that have been trained on a corpus of existing levels. The method is fast and simple, and produces levels that are recognisably in the same style as those in the corpus that it has been trained on. We use Super Mario Bros. as an example domain, and use a selection of the levels from the original game as a training corpus. We treat Mario levels as a left-to-right sequence of vertical level slices, allowing us to perform level generation in a setting with some formal similarities to n-gram-based text generation and music generation. In empirical results, we investigate the effects of corpus size and n (sequence length). While the applicability of the method might seem limited to the relatively narrow domain of 2D games, we argue that many games in effect have linear levels and n-grams could be used to good effect, given that a suitable alphabet can be found.

Procedural Content Generation Using Patterns as Objectives

In this paper we present a search-based approach for procedural generation of game levels that represents levels as sequences of micro-patterns and searched for meso-patterns. The micro-patterns are “slices” of original human-designed levels from an existing game, whereas the meso-patters are abstractions of common design patterns seen in the same levels. This method generates levels that are similar in style to the levels from which the original patterns were extracted, while still allowing for considerable variation in the geometry of the generated levels. The evolutionary method for generating the levels was tested extensively to investigate the distribution of micro-patterns used and meso-patterns found.

A multi-level level generator

Generating content at multiple levels of abstraction simultaneously is an open challenge in procedural content generation. Representing and automatically replicating the style of a human designer is another. This paper addresses both of these challenges through extending a previously devised methodology for pattern-based level generation. This method builds on an analysis of Super Mario Bros levels into three abstraction levels: micro-, meso- and macro-patterns. Micro-patterns are then used as building blocks in a search-based PCG approach that searches for macro-patterns, which are defined as combinations of meso-patterns. Results show that we can successfully generate levels that replicate the macro-patterns of selected input levels, and we argue that this constitutes an approach to automatically analysing and replicating style in level design.

Mixed-initiative procedural generation of dungeons using game design patterns

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.

Towards pattern-based mixed-initiative dungeon generation

Mixed-initiative Procedural Content Generation uses algorithms to assist human designers in the collaborative creation of game content. Different mixed-initiative approaches use different methods to engage with the design material while supporting the designers intentions. However, the designer runs the risk of misunderstanding the systems abilities and how to control them. In order to limit miscommunication during the design process, heuristics could be applied. In this paper we present a mixed-initiative tool for evolving dungeons with the aid of game design patterns as heuristics. The tool, the Evolutionary Dungeon Designer, uses a genetic algorithm that searches for levels containing game design patterns on two hierarchical levels of abstraction to express more complex gameplay in the game level. We evaluate the tool through a series of lab experiments and a user study conducted with professional game developers. Our results demonstrate that we are able to control the generation of the different patterns with the aid of design pattern-related input parameters, as well as identifying a number of features a design pattern-based mixed-initiative tool could benefit from.

Assessing aesthetic criteria in the evolutionary dungeon designer

The Evolutionary Dungeon Designer (EDD) [1] is as a mixed-initiative tool for creating dungeons for adventure games. Results from a user study with game developers positively evaluated EDD as a suitable framework for collaboration between human designers and PCG suggestions, highlighting these as time-saving and inspiring for creating dungeons [2]. Previous work on EDD identified the need of assessing aesthetic criteria as a key area for improvement in its PCG Engine. By upgrading the individual encoding system and the fitness evaluation in EDDs evolutionary algorithm, we present three techniques to preserve and account the designers aesthetic criteria during the dungeon generation process: the capability of locking sections for preserving custom aesthetic structures, as well as the measurement of symmetry and similarity in the provided suggestions.

Playing Together: The Player’s Repertoire, an Obstacle to Learning

Massively multiplayer online games have become a common research subject in game studies. Several of these studies have focused on how the player interacts with the game and other players through the game, but often the fact is neglected that there are other games besides MMOGs that allow players to interact with each other. Two-player off-line games, for example, also allow for interaction between players, both through the game and in the physical world. This chapter focuses on the improvement of skills while such a game is being played. We use interaction analysis to understand how the player learns to play the game and how to play together with someone else. By observing how players interact with each other and with the game in a setting with only two players, we find different learning situations than one would find in a single-player or MMOG environment. These learning situations show that the formulation of an understanding of a game and the incorporation of the game into the player’s repertoire are obstructed by the repertoire itself, and they show that players may have trouble adapting to a reflective playing style. This case study is part of a larger project on situated play with multiplayer off-line and colocated video games.