Video game designers iteratively improve player experience by play testing game software and adjusting its design. Deciding how to improve gameplay is difficult and time-consuming because designers lack an effective means for exploring decision alternatives and modifying a game’s mechanics. We aim to improve designer productivity and game quality by providing tools that speed-up the game design process. In particular, we wish to learn how patterns en- coding common game design knowledge can help to improve design tools. Micro-Machinations (MM) is a language and software library that enables game designers to modify a game’s mechanics at run-time. We propose a pattern-based approach for leveraging high-level design knowledge and facilitating the game design process with a game design assistant. We present the Mechanics Pattern Language (MPL) for encoding common MM structures and design intent, and a Mechanics Design Assistant (MeDeA) for analyzing, explaining and understanding existing mechanics, and generating, filtering, exploring and applying design alternatives for modifying mechanics. We implement MPL and MeDeA using the meta-programming language Rascal, and evaluate them by modifying the mechanics of a prototype of Johnny Jetstream, a 2D shooter developed at IC3D Media.
A level designer typically creates the levels of a game to cater for a certain set of objectives, or mission. But in procedural content generation, it is common to treat the creation of missions and the generation of levels as two separate concerns. This often leads to generic levels that allow for various missions. However, this also creates a generic impression for the player, because the potential for synergy between the objectives and the level is not utilised. Following up on the mission-space generation concept, as described by Dormans, we explore the possibilities of procedurally generating a level from a designer-made mission. We use a generative grammar to transform a mission into a level in a mixed-initiative design setting. We provide two case studies, dungeon levels for a rogue-like game, and platformer levels for a metroidvania game. The generators differ in the way they use the mission to generate the space, but are created with the same tool for content generation based on model transformations. We discuss the differences between the two generation processes and compare it with a parameterized approach.
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This study furthers game-based learning for circular business model innovation (CBMI), the complex, dynamic process of designing business models according to the circular economy principles. The study explores how game-play in an educational setting affects learning progress on the level of business model elements and from the perspective of six learning categories. We experimented with two student groups using our game education package Re-Organise. All students first studied a reader and a game role description and then filled out a circular business model canvas and a learning reflection. The first group, i.e., the game group, updated the canvas and the reflection in an interactive tutorial after gameplay. The control group submitted their updated canvas and reflection directly after the interactive tutorial without playing the game. The results were analyzed using text-mining and qualitative methods such as word co-occurrence and sentiment polarity. The game group created richer business models (using more waste processing technologies) and reflections with stronger sentiments toward the learning experience. Our detailed study results (i.e., per business model element and learning category) enhance understanding of game-based learning for circular business model innovation while providing directions for improving serious games and accompanying educational packages.
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