Automatic mapping of NES games with mappy

Osborn, Joseph C.; Summerville, Adam; Mateas, Michael

doi:10.1145/3102071.3110576

Cited by 16 publications

(11 citation statements)

References 15 publications

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“…Rather, we want to discover mechanics by using agent play-throughs, and by using the discovered mechanics, construct a mechanic graph. Methods for automatically identifying maps, mechanics and other characteristics of games given only an executable version of the game show promise for this kind of project [26]. As stated above, there are limitations to our system, and one priority is to improve it for GVG-AI games.…”

Section: Resultsmentioning

confidence: 99%

“…After the game is created, Game-O-Matic generates a tutorial page, explaining who the player will control, how to control them, and winning/losing conditions, by using the concept-map and relationships between objects within it. Mappy is a system which takes a Nintendo Entertainment System game and a sequence of buttons presses as input to generate an approximation of a linked map of rooms [26]. Mappy essentially attempts to create understanding of map levels from movement mechanics.…”

Section: Generative Methods For Tutorials and Game Mechanicsmentioning

confidence: 99%

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AtDELFI

Green

Khalifa

Barros

et al. 2018

Proceedings of the 13th International Conference on the Foundations of Digital Games

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This paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (AuTomatically DEsigning Legible, Full Instructions for games) was created to investigate procedural generation of instructions that teach players how to play video games. We present a representation of game rules and mechanics using a graph system as well as a tutorial generation method that uses said graph representation. We demonstrate the concept by testing it on games within the General Video Game Artificial Intelligence (GVG-AI) framework; the paper discusses tutorials generated for eight different games. Our findings suggest that a graph representation scheme works well for simple arcade style games such as Space Invaders and Pacman, but it appears that tutorials for more complex games might require higher-level understanding of the game than just single mechanics.

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Section: Resultsmentioning

confidence: 99%

Section: Generative Methods For Tutorials and Game Mechanicsmentioning

confidence: 99%

AtDELFI

Green

Khalifa

Barros

et al. 2018

Proceedings of the 13th International Conference on the Foundations of Digital Games

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“…Mega Man has had significantly fewer PCGML approaches applied to it in comparison to Super Mario Bros.. Some prior work has modeled Mega Man with machine learning without generating new Mega Man content [7,12]. Sarkar et al have modeled Mega Man levels along with levels from a large number of other games with Variational Autoencoders (VAEs) for the purpose of recombining this content to create entirely new types of content [14][15][16][17]33].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Learning For Mega Man Level Generation

Li,

Chen,

Xue

et al. 2021

Preprint

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Procedural content generation via machine learning (PCGML) is the process of procedurally generating game content using models trained on existing game content. PCGML methods can struggle to capture the true variance present in underlying data with a single model. In this paper, we investigated the use of ensembles of Markov chains for procedurally generating Mega Man levels. We conduct an initial investigation of our approach and evaluate it on measures of playability and stylistic similarity in comparison to a non-ensemble, existing Markov chain approach. CCS CONCEPTS• Computing methodologies → Ensemble methods.

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“…Mappy is a system which takes a Nintendo Entertainment System game and a sequence of buttons presses as input to generate an approximation of a linked map of rooms [25]. Mappy attempts to create map understanding from movement mechanics.…”

Section: Tutorial Generationmentioning

confidence: 99%

Generating levels that teach mechanics

Green

Khalifa

Barros

et al. 2018

Proceedings of the 13th International Conference on the Foundations of Digital Games

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The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.This section discusses frameworks and research relevant to our work. It starts with a description of the Mario AI framework, followed by a brief background on search based level generation and level generation for the Mario AI framework, and concluding with tutorials and tutorial generation. arXiv:1807.06734v4 [cs.AI] 1 Oct 2018 FDG18, August 7-10, 2018, Malmö, Sweden M. Green et al.

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Automatic mapping of NES games with mappy

Cited by 16 publications

References 15 publications

AtDELFI

AtDELFI

Ensemble Learning For Mega Man Level Generation

Generating levels that teach mechanics

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