A Progressive Approach to Content Generation

Shaker, Mohammad; Shaker, Noor; Togelius, Julian; Abou-Zleikha, Mohamed

doi:10.1007/978-3-319-16549-3_31

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…In addition, they included a section on the possible use of PCGML for mixed initiative and co-creativity. They refer to Tanagra [3], [4], Sentient Sketchbook [5], [6], and Ropossum [7]- [10] as earlier examples that do not use machine learning (ML) and mention inpainting [11] as a somewhat related work that uses PCGML and which could be transposed over to level generation. Petrovskaya et al [12] created a typology of applications designed for casual creators (see Section IV-A).…”

Section: Related Recent Studiesmentioning

confidence: 99%

On Mixed-Initiative Content Creation for Video Games

2022

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In this article, we present a survey of mixed-initiative methods for the creation of content for video games. We also propose a definition of what mixed initiative implies, as the term lacks a clear specification. The survey includes works not directly aimed at video games but which create content that can potentially be used in games, such as art programs utilizing mixed initiative. Furthermore, we highlight research areas that overlap wholly or partly with mixed initiative, such as casual creators, explainable artificial intelligence, or interactive evolutionary computation. We examine these and several other topics in the context of mixed initiative. Finally, we provide a catalogue of typical techniques and challenges connected with mixed initiative before considering future directions.

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Section: Related Recent Studiesmentioning

confidence: 99%

On Mixed-Initiative Content Creation for Video Games

2022

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“…Finally Shaker et al researched a progressive approach for content generation; this approach combines constructive and search-based approaches to create a system that is fast, flexible and reliable [45]. The progressive approach has two components: timeline generation, where GE is used to evolve a timeline of in-game events, and timeline simulation, where the timelines are simulated using an intelligent agent.…”

Section: H Physics Puzzlesmentioning

confidence: 99%

Procedural Puzzle Generation: A Survey

Kegel

Haahr

2020

IEEE Trans. Games

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Procedural Content Generation (PCG) for games has existed since the 1980s and is becoming increasingly important for creating gameworlds, backstory and characters across many genres, in particular open-world games such as Minecraft (2011) and No Man's Sky (2016). A particular challenge faced by such games is that the content and/or gameplay may become repetitive. Puzzles constitute an effective technique for improving gameplay by offering players interesting problems to solve, but the use of PCG for generating puzzles has been limited compared with its use for other game elements, and efforts have focused mainly on games that are strictly puzzle games, rather than creating puzzles to be incorporated into other genres. Nevertheless, a significant body of work exists, which allows puzzles of different types to be generated algorithmically, and there is scope for much more research into this area. This paper presents a detailed survey of existing work in PCG for puzzles, reviewing 32 methods within eleven categories of puzzles. For the purpose of analysis, the paper identifies a total of seven salient characteristics related to the methods, which are used to show commonalities and differences between techniques and to chart promising areas for future research.

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“…The search-based approach will look for solutions (in this case content) that are in accordance with the constraints. The search-based approach has a weakness in its speed, the search-based approach is much slower than the constructive approach and is too slow to use in real-time [7]. Research tried to combine the diversity, flexibility, and ability to guarantee the playability of a search-based approach with the speed of a constructive approach called the progressive approach.…”

Section: Introductionmentioning

confidence: 99%

Progressive Content Generation Based on Cyclic Graph for Generate Dungeon

Anshar

Sumiharto

Wibowo

2023

Indonesian J. Comput. Cybern. Syst.

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Dungeon is level in game consisting collection of rooms and doors with obstacles inside. To make good level, takes a lot of time. With Procedural Content Generation (PCG), dungeons can be created automatically. One of the approaches in PCG to create levels is progressive. Progressive approach produces timeline as representation of the interactions in the game. Timeline representation that is in the form of one straight line is good for endless runner, but for dungeon, the levels are linear. In this research, the timeline is changed to cyclic graph. Cyclic graph is formed using graph grammar algorithm. This research aims to build dungeon that has not linear and minimal dead ends. To eliminate linearity in dungeons, branching in dungeons needs to be formed. The steps carried out in this research are designing graph grammar rules, generating population of graphs, evaluating graphs with fitness values, and building dungeons. Four functions are used to determine the fitness value: shortest vertices, average duration, replayability, and variation. Dungeons produced with progressive approach manage to minimize linearity in dungeons. Dungeon formation is very dependent on the rule grammar that forms it. With the evaluation process, linear dungeons resulting from grammar rules can be minimized.

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A Progressive Approach to Content Generation

Cited by 7 publications

References 10 publications

On Mixed-Initiative Content Creation for Video Games

On Mixed-Initiative Content Creation for Video Games

Procedural Puzzle Generation: A Survey

Progressive Content Generation Based on Cyclic Graph for Generate Dungeon

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