With the growing use of automated content creation and computer-aided design tools in game development, there is potential for enhancing the design process through personalized interactions between the software and the game developer. This paper proposes designer modeling for capturing the designer's preferences, goals and processes from their interaction with a computer-aided design tool, and suggests methods and domains within game development where such a model can be applied. We describe how designer modeling could be integrated with current work on automated and mixed-initiative content creation, and envision future directions which focus on personalizing the processes to a designer's particular wishes.
Michael Cook, a 30-year-old senior research fellow at the University of Falmouth, has built an AI capable of imagining new video games from scratch. Cook calls the machine Angelina, a recursive acronym that stands for "A Novel Game-Evolving Labrat I've Named Angelina" (a joke that Cook says got old pretty quickly). Since its earliest form, in 2011, it has created hundreds of experimental video games, received acclaim in an international game-making competition, and had its work featured in a New York gallery exhibit.
The future of procedural content generation (PCG) lies beyond the dominant motivations of “replayability” and creating large environments for players to explore. This paper explores both the past and potential future for PCG, identifying five major lenses through which we can view PCG and its role in a game: data vs. process intensiveness, the interactive extent of the content, who has control over the generator, how many players interact with it, and the aesthetic purpose for PCG being used in the game. Using these lenses, the paper proposes several new research directions for PCG that require both deep technical research and innovative game design.
The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.
Many modern creative industrial processes rely on the collaboration between multiple humans, assisted by one or more computational systems, in a complex environment. However, most traditional systems lack the adaptability required to contribute in a flexible, co-creative manner, instead executing a fixed set of tasks in a preset time schedule. We believe games, especially cooperative games offer an ideal platform to conduct research in co-creativity. We present our motivation, preliminary work and future goals to study, build and measure game-inspired co-creative AI systems.