Learning Player Tailored Content From Observation: Platformer Level Generation from Video Traces using LSTMs

Abstract

A touted use of Procedural Content Generation is generating content tailored to specific players. Previous work has relied on human identification of player profile features which are then mapped to level generator features. We present a machine-learned technique to train generators on Super Mario Bros. videos, generating levels based on latent play styles learned from the video. We evaluate the generators in comparison to the original levels and a machine-learned generator trained using simulated players.

Procedural Content Generation via Knowledge Transformation (PCG-KT)

“…For example, in a lot of PCGML work, a tile-based representation of a level can be taken as the raw knowledge, as in Figure 1. This enables methods for learning about the level structure in discrete patterns (e.g., [11], [12]). Alternatively, if the process started from images of input levels and converted them into some ML-parsable representation like tiles, the input images would be considered the raw knowledge.…”

Leveraging Multi-Layer Level Representations for Puzzle-Platformer Level Generation

“…Though this is a straightforward concept, it opens up the possibility for more faithful and deep level representation than is possible with previous representations which only capture structural information (Guzdial and Riedl 2016;Snodgrass and Ontañón 2016b) and occasionally player path information (Summerville et al 2016a). For example, in this paper in addition to a structural and a player path layer, we use a section layer which signifies different sections of the level.…”

Curve-Based Level Generation for a Full-Body VR Rhythm Game

“…to craft a level from scratch. Level generation strategies for two-dimensional platformers often generate levels based upon specific elements of gameplay, such as the rhythm of a player's movement through a level (Smith et al 2009) or play styles (Summerville et al 2016). Approaches to rhythm game level generation have leveraged techniques including the use of neural networks (Donahue, Lipton, and McAuley 2017;Tsujino and Yamanishi 2018) and selection from a set of prerecorded moves (Martin et al 2019).…”