Deriving Quests from Open World Mechanics

Alexander, Ryan; Martens, Chris

doi:10.48550/arxiv.1705.00341

Cited by 1 publication

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“…De Messentier Silva et al [9][10][11] used various search methods to create effective beginner strategies for Blackjack and Poker. Alexander et al [1] turned Minecraft (Mojang 2009) mechanics into action graphs representing the player experience, and created quests and achievements based off those actions. Game-O-Matic [38] generates arcade style games and instructions using a story-based concept-map.…”

Section: Tutorial Generationmentioning

confidence: 99%

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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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Section: Tutorial Generationmentioning

confidence: 99%

“…Figure1: A skill atom for learning how to jump in any generic game, in the order of action (button), simulation (jump and collide), feedback (animation on screen), and modeling ("I can jump! ")1 …”

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confidence: 99%