ConceptThe goal of the worksop/tutorial is to introduce participants to the fundamentals of Procedural Content Generation (PCG) based on generative grammars, have them experience an example of such a system first-hand, and discuss the potential of this approach for various areas of procedural content generation for games. The principles and examples are based on Ludoscope, a software tool developed at the HvA by Dr. Joris Dormans, e.a.Duration: 2 hoursOverviewWe will use the first 30 minutes to explain the basics of how to use generative grammars to generate levels. The principles of these grammars and model transformations will be demonstrated by means of the level generation system of Spelunky, which we have modeled in Ludoscope.Spelunky focuses solely on the generation of geometry, but grammar-based systems can also be used to transform more abstract concepts of level design into level geometry. In the next hour, the participants will be able to get some hands-on experience with Ludoscope. The assignment will be to generate a Mario-like level based on specific requirements, adapted to the interests of workshop participants.Finally, we are interested in the participants’ evaluation of this approach to PCG. We will use the last 20 minutes to discuss alternative techniques, and possible applications to other areas of PCG, like asset creation, scripting and game generation.Workshop participants are asked to bring a (PC) laptop to work on during the workshop, and are encouraged to work in pairs.
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Grammar-based procedural level generation raises the productivity of level designers for games such as dungeon crawl and platform games. However, the improved productivity comes at cost of level quality assurance. Authoring, improving and maintaining grammars is difficult because it is hard to predict how each grammar rule impacts the overall level quality, and tool support is lacking. We propose a novel metric called Metric of Added Detail (MAD) that indicates if a rule adds or removes detail with respect to its phase in the transformation pipeline, and Specification Analysis Reporting (SAnR) for expressing level properties and analyzing how qualities evolve in level generation histories. We demonstrate MAD and SAnR using a prototype of a level generator called Ludoscope Lite. Our preliminary results show that problematic rules tend to break SAnR properties and that MAD intuitively raises flags. MAD and SAnR augment existing approaches, and can ultimately help designers make better levels and level generators.
This paper addresses the procedural generation of levels for collaborative puzzle-platform games. To address this issue, we distinguish types of multiplayer interaction, focusing on two-player collaboration, and identify relevant game mechanics for a puzzle-platform game, addressing player movement, interaction with moving game objects, and physical interaction involving both players. These are further formalized as game design patterns. To test the feasibility of the approach, a level generator has been implemented based on a rule-based approach, using the existing tool called Ludoscope and a prototype game developed in the Unity game engine. The level generation procedure results in over 3.7 million possible playable level variations that can be generated automatically. Each of these levels encourages or even requires both players to engage in collaborative gameplay.
