5 research outputs found
Deriving Quests from Open World Mechanics
Open world games present players with more freedom than games with linear
progression structures. However, without clearly-defined objectives, they often
leave players without a sense of purpose. Most of the time, quests and
objectives are hand-authored and overlaid atop an open world's mechanics. But
what if they could be generated organically from the gameplay itself? The goal
of our project was to develop a model of the mechanics in Minecraft that could
be used to determine the ideal placement of objectives in an open world
setting. We formalized the game logic of Minecraft in terms of logical rules
that can be manipulated in two ways: they may be executed to generate graphs
representative of the player experience when playing an open world game with
little developer direction; and they may be statically analyzed to determine
dependency orderings, feedback loops, and bottlenecks. These analyses may then
be used to place achievements on gameplay actions algorithmically.Comment: To appear at Foundations of Digital Games (FDG) 201
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
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