A procedural procedural level generator generator

Procedural content generation (PCG) is concerned with automatically generating game content, such as levels, rules, textures and items. But could the content generator itself be seen as content, and thus generated automatically? This would be very useful if one wanted to avoid writing a content generator for a new game, or if one wanted to create a content generator that generates an arbitrary amount of content with a particular style or theme. In this paper, we present a procedural procedural level generator generator for Super Mario Bros. It is an interactive evolutionary algorithm that evolves agent based level generators. The human user makes the aesthetic judgment on what generators to prefer, based on several views of the generated levels including a possibility to play them, and a simulation-based estimate of the playability of the levels. We investigate the characteristics of the generated levels, and to what extent there is similarity or dissimilarity between levels and between generators.peer-reviewe

Analysis and application of rhythm in the design of 2D platformer levels

Abstract. The video game industry has grown quickly from its humble beginnings to one of the largest entertainment industries in the world. Fuelled by the continuous advancements in technology, the quality and quantity of content in AAA video games continues to rise along with customer expectations. But with the ever-higher ambitions, the development budgets and durations rise with them, making the cycle unsustainable on the long run. Procedural content generation is a technique that has the potential of helping break the cycle. The automatic generation of game content, such as levels, could help game developers reach the desired quantity of content with a fraction of the time and money required. However, commercial applications of procedural content generation so far have been largely limited in scope and lacking in quality, with the more successful cases being found in smaller budget indie games. In this study, the possibility to use the idea of rhythm in guiding procedural level generation towards better quality was studied. Using a design science research approach, the gameplay rhythm of original Super Mario Bros. levels was extracted and used to build a rhythm-based procedural 2D platformer level generator. The nature of the generated levels was investigated by computational metrics, and the quality of them was evaluated by a series of playtests. It was found that the existing platformer levels included an extractable rhythm. The rhythm-based level generator that was built upon the found rhythm data produced levels that were closely on par with the original levels, indicating that rhythm has potential applications in informing how a procedural content generator could create more meaningful and higher quality content. Finally, this experimental approach in incorporating music theory to procedural content generation opens up many interesting new avenues for future research

Procedural Generation of 2D Games

Tese de mestrado, Engenharia Informática (Interação e Conhecimento) Universidade de Lisboa, Faculdade de Ciências, 2020The main objective of this project is to develop a procedural generator of levels for 2D games, with the capacity of adapting the difficulty of the levels to a player’s skill in a specific game. Thus, in order to implement a procedural generator with the previously mentioned features, we intend to combine two techniques: procedural content generation and dynamic difficulty adjustment. Procedural content generation is a technique which has the purpose of creating content for a game. The game content generated can be anything related to the video-game in question (e.g. characters, items, terrain, levels). Dynamic difficulty adjustment is the name of the technique used to make adjustments to the game’s difficulty, depending on the overall progress of a player in a particular level. The procedural content generator developed uses the idea of rhythms of a level as its basis (Smith et al., 2009). This approach consists on describing a level as a sequence of actions that must be done to successfully conclude it. Our methodology differs from the classical rhythm-based approach, because instead of a sequence of single actions we rep resent a level as a sequence of classes of actions. A class of actions is a group of actions that have the same assumed difficulty, which is defined by a mechanic description (what keys to press to perform an action). For the generation of these sequences of classes of actions, it is used a genetic algorithm whose fitness function is able to evaluate the difficulty of a sequence, which allows it to generate rhythms for diverse levels with different difficulties. After the rhythm generation process, the resulting sequences of classes of actions are going to be passed as a parameters to a geometry generator, that is going to associate each of the class of actions to a level chunk, having, in the end, a new playable level (a group of level chunks). This approach was then tested with different games to demonstrate the generator’s capacity to generalize and, to prove our definitions of difficulty, we made some tests using search algorithms and human players to make this evaluation

Framing tension for game generation

Emotional progression in narratives is carefully structured by human authors to create unexpected and exciting situations, often culminating in a climactic moment. This paper explores how an autonomous computational designer can create frames of tension which guide the procedural creation of levels and their soundscapes in a digital horror game. Using narrative concepts, the autonomous designer can describe an intended experience that the automated level generator must adhere to. The level generator interprets this intent, bound by the possibilities and constraints of the game. The tension of the generated level guides the allocation of sounds in the level, using a crowdsourced model of tension.peer-reviewe

PCGRL: Procedural Content Generation via Reinforcement Learning

We investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, where level design is framed as a game, and the content generator itself is learned. By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action so that the expected final level quality is maximized. This approach can be used when few or no examples exist to train from, and the trained generator is very fast. We investigate three different ways of transforming two-dimensional level design problems into Markov decision processes and apply these to three game environments.Comment: 7 pages, 7 figures, 1 table, published at AIIDE202

Non-interactive modeling tools and support environment for procedural geometry generation

This research examines procedural modeling in the eld of computer graphics. Procedural modeling automates the generation of objects by representing models as procedures that provide a description of the process required to create the model. The problem we solve with this research is the creation of a procedural modeling environment that consists of a procedural modeling language and a set of non-interactive modeling tools. A goal of this research is to provide comparisons between 3D manual modeling and procedural modeling, which focus on the modeling strategies, tools and model representations used by each modeling paradigm. A procedural modeling language is presented that has the same facilities and features of existing procedural modeling languages. In addition, features such as caching and a pseudorandom number generator is included, demonstrating the advantages of a procedural modeling paradigm. The non-interactive tools created within the procedural modeling framework are selection, extrusion, subdivision, curve shaping and stitching. In order to demonstrate the usefulness of the procedural modeling framework, human and furniture models are created using this procedural modeling environment. Various techniques are presented to generate these objects, and may be used to create a variety of other models. A detailed discussion of each technique is provided. Six experiments are conducted to test the support of the procedural modeling benets provided by this non- interactive modeling environment. The experiments test, namely parameterisation, re-usability, base-shape independence, model complexity, the generation of reproducible random numbers and caching. We prove that a number of distinct models can be generated from a single procedure through the use parameterisation. Modeling procedures and sub-procedures are re-usable and can be applied to different models. Procedures can be base-shape independent. The level of complexity of a model can be increased by repeatedly applying geometry to the model. The pseudo-random number generator is capable of generating reproducible random numbers. The caching facility reduces the time required to generate a model that uses repetitive geometry

Online Game Level Generation from Music

Game consists of multiple types of content, while the harmony of different content types play an essential role in game design. However, most works on procedural content generation consider only one type of content at a time. In this paper, we propose and formulate online level generation from music, in a way of matching a level feature to a music feature in real-time, while adapting to players' play speed. A generic framework named online player-adaptive procedural content generation via reinforcement learning, OPARL for short, is built upon the experience-driven reinforcement learning and controllable reinforcement learning, to enable online level generation from music. Furthermore, a novel control policy based on local search and k-nearest neighbours is proposed and integrated into OPARL to control the level generator considering the play data collected online. Results of simulation-based experiments show that our implementation of OPARL is competent to generate playable levels with difficulty degree matched to the ``energy'' dynamic of music for different artificial players in an online fashion