Punch Out Model Synthesis: A Stochastic Algorithm for Constraint Based Tiling Generation

TL;DR

POMS is a stochastic algorithm for constraint-based tiling generation that efficiently handles large problems, minimizes setup assumptions, and reduces solution bias in tiled level design.

Contribution

It introduces POMS, a novel stochastic method that improves problem size handling and bias mitigation compared to existing algorithms like MMS and WFC.

Findings

Successfully applied to various tile sets

Effective in large problem scenarios

Highlights importance of tile correlation length

Abstract

As an artistic aid in tiled level design, Constraint Based Tiling Generation (CBTG) algorithms can help to automatically create level realizations from a set of tiles and placement constraints. Merrell's Modify in Blocks Model Synthesis (MMS) and Gumin's Wave Function Collapse (WFC) have been proposed as Constraint Based Tiling Generation (CBTG) algorithms that work well for many scenarios but have limitations in problem size, problem setup and solution biasing. We present Punch Out Model Synthesis (POMS), a Constraint Based Tiling Generation algorithm, that can handle large problem sizes, requires minimal assumptions for setup and can help mitigate solution biasing. POMS attempts to resolve indeterminate grid regions by trying to progressively realize sub-blocks, performing a stochastic boundary erosion on previously resolved regions should sub-block resolution fail. We highlight the results of running a reference implementation on different tile sets and discuss a tile correlation length, implied by the tile constraints, and its role in choosing an appropriate block size to aid POMS in successfully finding grid realizations.