A Practical View on Procedural Generation: Benefits, Capabilities, and Usability

Abstract

Procedural generation is widely applied, from accelerating development of large and detailed virtual worlds to providing highly varied game content during gameplay. However, for procedural generators to be widely adopted, developers and artists must be able to select a suitable approach and contextualize it with respect to artifact design objectives. This paper presents an overview of the four main procedural generation approaches: constructive, search-based, solver-based, and machine learning. The paper also introduces three lenses for understanding how to use procedural generators in practice: benefits, capabilities, and usability. The benefits lens examines what objectives can be achieved with procedural generators. The capabilities lens distinguishes between artist-assisting procedural generation tools, procedural materials, automated designers, and expert systems capable of generating reliable artifacts. Finally, the usability lens addresses how to integrate procedural generators into practice and how users interact with them. Examples of generation approaches from academia and commercial works are provided to illustrate each lens. This review synthesizes the characteristics, benefits and drawbacks of various procedural generation methods, helping developers choose the most appropriate technique based on a particular use case. A decision support matrix is also provided to illustrate how to apply this practical view in different development scenarios.