Procedural content generation in pervasive games: state of affairs, mistakes, and successes

Purpose In ubiquitous games, sensor data and the player’s dynamic profile personalize the experience, adapting scenarios and difficulty to the player’s context. However, manually creating custom virtual environments becomes impractical due to the scalability required in the real world, where gameplay depends on the player’s environment and various points of interest. Procedural content generation (PCG) naturally emerges as an automated solution. This study aims to review the state of the art among pervasive games that use procedural generation techniques. Design/methodology/approach The authors performed a systematic review of the literature on PCG techniques in ubiquitous games. The research methodology included initial database searches, forward snowballing and backward snowballing. Findings The authors selected 40 articles published from 2008 to 2022 after analyzing 1,017 PCG articles. Many of these paper use dynamic difficulty generation methods that affect game customization, balance and playability. The majority of titles are in the area of education and require rigorous evaluation. Some articles mention the use of PCG but provide limited details of algorithms or processes. Research limitations/implications The low presence of recent articles stands out, which can lead to two conclusions: poor use of indexing of articles in the area or low amount of research over these years. One of the search bases did not enter due to the number of keywords used. Practical implications The paper is a guide for researchers in the area of pervasive games who are interested in using PCG techniques in their games. Originality/value This approach revealed articles combining PCG and ubiquitous games, with no previous systematic reviews at this intersection.