Evolving Cellular Automata for Maze Generation

Pech, Andrew; Hingston, Philip; Mašek, Martin; Lam, Chiou Peng

doi:10.1007/978-3-319-14803-8_9

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…We used cellular automata to generate maze-like game levels in our main pipeline. Usage of cellular automata on procedural generation of game levels is also one of the popular research areas over the last decade [33], [34], [35]. The main reasons for the usage of cellular automata are their simplicity and reliability [33].…”

Section: Literature Reviewmentioning

confidence: 99%

Verifying Maze-Like Game Levels With Model Checker SPIN

Tekik,

Surer,

Betin Can

2022

IEEE Access

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This study presents a framework that procedurally generates maze-like levels and leverages an automated verification technique called model checking to verify and produce a winning action sequence for that level. By leveraging the counterexample generation feature of the SPIN model checker, one or more solutions to the level-in-test are found, and the solutions are animated using a video game description language, PyVGDL. The framework contains four behavioral templates developed to model the logic of maze-like puzzle games in the modeling language of SPIN. These models automatically are tailored according to the level-in-test. To show the proposed methodology's effectiveness, we conducted five different experiments. These experiments include performance comparisons in level-solving between the proposed and existing methodologies -A* Search and Monte Carlo Tree Search-and demonstrations of the use of the proposed approach to check a game level with respect to requirements. This study also proposes a pipeline to generate maze-like puzzle levels with two levels of cellular automata.INDEX TERMS Formal verification, model checking, procedural content generation, puzzle games, video game description language.

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Section: Literature Reviewmentioning

confidence: 99%

Verifying Maze-Like Game Levels With Model Checker SPIN

Tekik,

Surer,

Betin Can

2022

IEEE Access

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“…This extends even to procedural textures developed originally for computer graphics. Some do not have any basis in physics, such as maze generation using evolving cellular automata [ 135 ]—even though the results share some characteristics with Turing patterns. A round tile pattern can be created by recursively solving the mathematical three circle touching problem and morphological post-processing ( discs , Figure 2 ).…”

Section: Artificial Spm Datamentioning

confidence: 99%

Synthetic Data in Quantitative Scanning Probe Microscopy

Nečas

Klapetek

2021

Nanomaterials

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Synthetic data are of increasing importance in nanometrology. They can be used for development of data processing methods, analysis of uncertainties and estimation of various measurement artefacts. In this paper we review methods used for their generation and the applications of synthetic data in scanning probe microscopy, focusing on their principles, performance, and applicability. We illustrate the benefits of using synthetic data on different tasks related to development of better scanning approaches and related to estimation of reliability of data processing methods. We demonstrate how the synthetic data can be used to analyse systematic errors that are common to scanning probe microscopy methods, either related to the measurement principle or to the typical data processing paths.

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“…Hendrawan [15] used Growing Tree algorithm to develop an Android game based on a maze. Perfect maze generation can also be done using recursive methods such as recursive backtracking [16] and recursive division [17]. Perhaps the closest maze generation method to this study is Prim and Kruskal algorithm.…”

Section: Related Workmentioning

confidence: 99%

Non-perfect maze generation using Kruskal algorithm

Ihsan

Suhaimi

Ramli

et al. 2021

J. Nat.

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A non-perfect maze is a maze that contains loop or cycle and has no isolated cell. A non-perfect maze is an alternative to obtain a maze that cannot be satisfied by perfect maze. This paper discusses non-perfect maze generation with two kind of biases, that is, horizontal and vertical wall bias and cycle bias. In this research, a maze is modeled as a graph in order to generate non-perfect maze using Kruskal algorithm modifications. The modified Kruskal algorithm used Fisher Yates algorithm to obtain a random edge sequence and disjoint set data structure to reduce process time of the algorithm. The modification mentioned above are adding edges randomly while taking account of the edge’s orientation, and by adding additional edges after spanning tree is formed. The algorithm designed in this research constructs an non-perfect maze with complexity of where and denote vertex and edge set of an grid graph, respectively. Several biased non-perfect mazes were shown in this research by varying its dimension, wall bias and cycle bias.

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Evolving Cellular Automata for Maze Generation

Cited by 12 publications

References 6 publications

Verifying Maze-Like Game Levels With Model Checker SPIN

Verifying Maze-Like Game Levels With Model Checker SPIN

Synthetic Data in Quantitative Scanning Probe Microscopy

Non-perfect maze generation using Kruskal algorithm

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