10,027 research outputs found
Generating Levels That Teach Mechanics
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.Comment: 8 pages, 7 figures, PCG Workshop at FDG 2018, 9th International
Workshop on Procedural Content Generation (PCG2018
AI Researchers, Video Games Are Your Friends!
If you are an artificial intelligence researcher, you should look to video
games as ideal testbeds for the work you do. If you are a video game developer,
you should look to AI for the technology that makes completely new types of
games possible. This chapter lays out the case for both of these propositions.
It asks the question "what can video games do for AI", and discusses how in
particular general video game playing is the ideal testbed for artificial
general intelligence research. It then asks the question "what can AI do for
video games", and lays out a vision for what video games might look like if we
had significantly more advanced AI at our disposal. The chapter is based on my
keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad
audience.Comment: in Studies in Computational Intelligence Studies in Computational
Intelligence, Volume 669 2017. Springe
AtDelfi: Automatically Designing Legible, Full Instructions For Games
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.Comment: 10 pages, 11 figures, published at Foundations of Digital Games
Conference 201
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