Procedural Level Design for Platform Games

There are several approaches in the literature for automatically generating Infinite Mario Bros levels. The evaluation of such approaches is often performed solely with computational metrics such as leniency and linearity. While these metrics are important for an initial exploratory evaluation of the content generated, it is not clear whether they are able to capture the player's perception of the content generated. In this paper we evaluate several of the commonly used computational metrics. Namely, we perform a systematic user study with procedural content generation systems and compare the insights gained from our user study with those gained from analyzing the computational metric values. The results of our experiment suggest that current computational metrics should not be used in lieu of user studies for evaluating content generated by computer programs.

Section: Other Evaluation Strategiesmentioning

confidence: 99%

An Empirical Evaluation of Evaluation Metrics of Procedurally Generated Mario Levels

Mariño

Reis

Lelis

2021

“…A lot of work has been performed previously on procedural content generation (Togelius et al 2011) and level generation for platform games, especially Super Mario Bros (Smith et al 2009;2011;Sorenson and Pasquier 2010a;Jennings-Teats, Smith, and Wardrip-Fruin 2010;Compton and Mateas 2006). A variety of different techniques have been used, with some systems incorporating the player's input (Nygren et al 2011;Yannakakis and Togelius 2011), or a human level designer's input (Mawhorter and Mateas 2010).…”

Section: Related Workmentioning

confidence: 99%

The Gold Standard: Automatically Generating Puzzle Game Levels

Williams-King

Denzinger

Aycock

et al. 2021

KGoldrunner is a puzzle-oriented platform game with dynamic elements. This paper describes Goldspinner, an automatic level generation system for KGoldrunner. Goldspinner has two parts: a genetic algorithm that generates candidate levels, and simulations that use an AI agent to attempt to solve the level from the player's perspective. Our genetic algorithm determines how "good" a candidate level is by examining many different properties of the level, all based on its static aspects. Once the genetic algorithm identifies a good candidate, simulations are performed to evaluate the dynamic aspects of the level. Levels that are statically good may not be dynamically good (or even solvable), making simulation an essential aspect of our level generation system. By carefully optimizing our genetic algorithm and simulation agent we have created an efficient system capable of generating interesting levels in real time.

“…Using beats to generate new platformer levels has been well explored. (Compton and Mateas 2006) uses a context free grammar to create new platformer levels, (Smith, Whitehead, and Mateas 2011;Smith et al 2009) uses a constraint solver to find a set of rhythm groups that fit a set of constraints, then finds a set of geometry that fits those rhythm group's constraints.…”

Section: Related Workmentioning

confidence: 99%

“…Metaphorically considering single obstacles or player actions as a 'beat' in time has been very productive for platformer level generation. Both (Compton andMateas 2006) and(Smith, Cha, andWhitehead 2008) derive a concept of rhythm from beats, where beats correspond to player actions to progress past level features.It does not seem beyond the pale to want to take the platformer idea of beat and see how well it fits in with a musical idea of beat. This would allow us to use techniques for music analysis and apply them to platformers.…”

mentioning

confidence: 99%

See 1 more Smart Citation

What Does Bach Have in Common with World 1-1: Automatic Platformer Gestalt Analysis

Pagnutti

2021

Platformer level generation has often used a beat metaphor to relate to how players interact with level geometry. However, this conceptualization of beats is different from the musical concept of `beat', limiting the utility of theories and tools developed in music analysis for platformer levels. A gameplay gestalt, a pattern of interaction that the player enacts or performs in order to make progress in a game, may fit the beat metaphor. By taking a very similar lens and viewing players playing platformer levels as enacting a series of gameplay gestalts through time, gestalt music analysis (GMA) does fit into the platformer domain. This paper details work on transforming a GMA model to work with the Platformer Experience Dataset (PED), and some promising first results of the transformed model.