Procedural Content Generation for Game Props? A Study on the Effects on User Experience

Abstract

This work demonstrates the potentials of procedural content generation (PCG) for games, focusing on the generation of specific graphic props (reefs) in an explorer game. We briefly portray the state-of-the-art of PCG and compare various methods to create random patterns at runtime. Taking a step towards the game industry, we describe an actual game production and provide a detailed pseudocode implementation showing how Perlin or Simplex noise can be used efficiently. In a comparative study, we investigate two alternative implementations of a decisive game prop: once created traditionally by artists and once generated by procedural algorithms. 41 test subjects played both implementations. The analysis shows that PCG can create a user experience that is significantly more realistic and at the same time perceived as more aesthetically pleasing. In addition, the ever-changing nature of the procedurally generated environments is preferred with high significance, especially by players aged 45 and above.

Can’t see the Forest for the Trees: Perceiving Realism of Procedural Generated Trees in First-Person Games

“…Noghani et al [15] argue that PCG, such as vegetation and buildings, can enhance the realistic appearance and 'believability' of a game environment. This conclusion was also supported by Korn et al [8] whose findings suggest that players are perceiving the game as more realistic and aesthetically pleasing when using procedural generated graphical props. PCG can also be used to adapt the game world.…”

“…A wide range of techniques have been proposed for vegetation distribution. Noise algorithms have been used as an alternative to recursive functions, because they look more natural than white noise [8]. Onrust et al [16] proposed a grid based ecological distribution that places different plant species according to geometrical variations of the terrain's relief.…”

“…No significant two-way interaction between distribution and growth factors was found, F(1, 19) = 0.347, p = Figure 13. Box-plots visualizing the results of the six 9-point rating scales (1)(2)(3)(4)(5)(6)(7)(8)(9) in items in terms of medians (-), means (x),inter-quartile ranges, and minimum and maximum ratings. 10 EAI Endorsed Transactions on Creative Technologies 12 2020 -03 2021 | Volume 8 | Issue 26 | e4 0.563, and no significant main effect of distribution was identified, F(1, 19) = 0.990, p = 0.332.…”

IA Aplicada a Jogos Digitais: Oportunidades e Desafios no Campo da Educação

“…[Togelius et al 2011] fala sobre a geração procedural de conteúdo (PCG) que é a criação de conteúdo realizada através de algoritmos e com isso ajudar a reduzir a produção de conteúdo para um jogo, pois a máquina seria responsável por criar o necessário para o jogo. Com isso, alguns outros benefícios são encontrados conforme [Korn et al 2017]: a agilidade na produção; redução dos custos; alto valor de replay ; proporcionar novas experiências ao usuários.…”

Geração Procedural de Conteúdo para Criação de Fases de Jogos Educativos usando Gramática

“…A PCG se refere à criação de conteúdo realizada por meio de algoritmos [Togelius et al 2011]. Os usos das técnicas de PCG apresentam diversos benefícios como: agilidade na produção; redução dos custos; alto valor de rejogabilidade; alta variação no jogo proporcionando novas experiências aos usuários; adaptação automática da dificuldade do jogo de acordo com o jogador [Korn et al 2017]; redução do custo de memória [Stephenson e Renz 2016]; e auxílio aos designers servindo como fonte de inspiração [Moghadam e Rafsanjani 2017]. Por isso, alguns projetos comerciais e de pesquisa na área de jogos digitais vem optando por utilizar alguma técnica de PCG, ou chegam até mesmo a desenvolver sua própria PCG para atender necessidades específicas do projeto.…”