32,473 research outputs found
Generative Design in Minecraft (GDMC), Settlement Generation Competition
This paper introduces the settlement generation competition for Minecraft,
the first part of the Generative Design in Minecraft challenge. The settlement
generation competition is about creating Artificial Intelligence (AI) agents
that can produce functional, aesthetically appealing and believable settlements
adapted to a given Minecraft map - ideally at a level that can compete with
human created designs. The aim of the competition is to advance procedural
content generation for games, especially in overcoming the challenges of
adaptive and holistic PCG. The paper introduces the technical details of the
challenge, but mostly focuses on what challenges this competition provides and
why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018
proceedings, as part of the workshop on Procedural Content Generatio
Generative Design in Minecraft: Chronicle Challenge
© 2016 ACC 2019We introduce the Chronicle Challenge as an optional addition to the Settlement Generation Challenge in Minecraft. One of the foci of the overall competition is adaptive procedural content generation (PCG), an arguably under-explored problem in computational creativity. In the base challenge, participants must generate new settlements that respond to and ideally interact with existing content in the world, such as the landscape or climate. The goal is to understand the underlying creative process, and to design better PCG systems. The Chronicle Challenge in particular focuses on the generation of a narrative based on the history of a generated settlement, expressed in natural language. We discuss the unique features of the Chronicle Challenge in comparison to other competitions, clarify the characteristics of a chronicle eligible for submission and describe the evaluation criteria. We furthermore draw on simulation-based approaches in computational storytelling as examples to how this challenge could be approached.Peer reviewe
A spatially-structured PCG method for content diversity in a Physics-based simulation game
This paper presents a spatially-structured evolutionary algorithm (EA) to procedurally generate game maps of di ferent levels of di ficulty to be solved, in Gravityvolve!, a physics-based simulation videogame that we have implemented and which is inspired by the n-
body problem, a classical problem in the fi eld of physics and mathematics. The proposal consists of a steady-state EA whose population is partitioned into three groups according to the di ficulty of the generated content (hard, medium or easy) which can be easily adapted to handle the automatic creation of content of diverse nature in other games. In addition, we present three fitness functions, based on multiple criteria (i.e:, intersections, gravitational acceleration and simulations), that were used experimentally to conduct the search process for creating a database of
maps with di ferent di ficulty in Gravityvolve!.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
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
A quantum procedure for map generation
Quantum computation is an emerging technology that promises a wide range of
possible use cases. This promise is primarily based on algorithms that are
unlikely to be viable over the coming decade. For near-term applications,
quantum software needs to be carefully tailored to the hardware available. In
this paper, we begin to explore whether near-term quantum computers could
provide tools that are useful in the creation and implementation of computer
games. The procedural generation of geopolitical maps and their associated
history is considered as a motivating example. This is performed by encoding a
rudimentary decision making process for the nations within a quantum procedure
that is well-suited to near-term devices. Given the novelty of quantum
computing within the field of procedural generation, we also provide an
introduction to the basic concepts involved.Comment: To be published in the proceedings of the IEEE Conference on Game
Modern Trends in the Automatic Generation of Content for Video Games
Attractive and realistic content has always played a crucial
role in the penetration and popularity of digital games, virtual
environments, and other multimedia applications. Procedural content
generation enables the automatization of production of any type of game
content including not only landscapes and narratives but also game
mechanics and generation of whole games. The article offers a
comparative analysis of the approaches to automatic generation of
content for video games proposed in last five years. It suggests a new
typology of the use of procedurally generated game content comprising of
categories structured in three groups: content nature, generation process,
and game dependence. Together with two other taxonomies – one of
content type and the other of methods for content generation – this
typology is used for comparing and discussing some specific approaches to
procedural content generation in three promising research directions
based on applying personalization and adaptation, descriptive languages,
and semantic specifications
DATA Agent
This paper introduces DATA Agent, a system which creates murder
mystery adventures from open data. In the game, the player
takes on the role of a detective tasked with finding the culprit of
a murder. All characters, places, and items in DATA Agent games
are generated using open data as source content. The paper discusses
the general game design and user interface of DATA Agent,
and provides details on the generative algorithms which transform
linked data into different game objects. Findings from a user study
with 30 participants playing through two games of DATA Agent
show that the game is easy and fun to play, and that the mysteries
it generates are straightforward to solve.peer-reviewe
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