Abstract: "Procedural content generation (PCG) is concerned
with automatically generating game content, such as levels,
rules, textures and items. But could the content generator itself
be seen as content, and thus generated automatically? This
would be very useful if one wanted to avoid writing a content
generator for a new game, or if one wanted to create a content
generator that generates an arbitrary amount of content with a
particular style or theme. In this paper, we present a procedural
procedural level generator generator for Super Mario Bros.
It is an interactive evolutionary algorithm that evolves agentbased
level generators. The human user makes the aesthetic
judgment on what generators to prefer, based on several views
of the generated levels including a possibility to play them, and
a simulation-based estimate of the playability of the levels. We
investigate the characteristics of the generated levels, and to
what extent there is similarity or dissimilarity between levels
and between generators."
"This dissertation presents the use of procedural content generation to create expressive design tools: content generators that are accessible to designers, supporting the creation of new kinds of design tools and enabling the exploration of a new genre of game involving the deep integration of procedural content generation into game mechanics and aesthetics. The first of these tools is Tanagra, the first ever AI-assisted level design tool that supports a designer creating levels for 2D platforming games. Tanagra guarantees that levels created in the tool are playable, and provides the designer with the ability to modify generated levels
and directly control level pacing. The second tool is Launchpad, which supports a designer controlling both component and pacing features of generated levels; its companion game Endless Web uses the generator to create an infinite world for players to explore and alter through their choices. Endless Web is one of a handful of games in a new genre enabled by content generation: PCG-based games. Finally, this dissertation presents a novel method for understanding, visualizing, and comparing a generator's expressive range, thus allowing designers to understand the implications of decisions they will make during the design process."
"This paper describes the creation of the game Endless Web, a 2D platforming game in which the player's actions determine the ongoing creation of the world she is exploring. Endless Web is an example of a PCG-based game: it uses procedural content generation (PCG) as a mechanic, and its PCG system, Launchpad, greatly influenced the aesthetics of the game. All of the player's strategies for the game revolve around the use of procedural content generation. Many design challenges were encountered in the design and creation of Endless Web, for both the game and modifications that had to be made to Launchpad. These challenges arise largely from a loss of fine-grained control over the player's experience; instead of being able to carefully craft each element the player can interact with, the designer must instead craft algorithms to produce a range of content the player might experience. In this paper we provide a definition of PCG-based game design and describe the challenges faced in creating a PCG-based game. We offer our solutions, which impacted both the game and the underlying level generator, and identify issues which may be particularly important as this area matures."
Abstract: "Procedural content generation and design patterns could potentially
be combined in several dierent ways in game design.
This paper discusses how to combine the two, using
automatic platform game level design as an example. The
paper also present work towards a pattern-based level generator
for Super Mario Bros, namely an analysis of the levels
of the original Super Mario Bros game into 23 dierent patterns."
Abstract :"Procedural content generation (PCG), the algorithmic creation
of game content with limited or indirect user input, has
much to offer to game design. In recent years, it has become
a mainstay of game AI, with significant research being put towards
the investigation of new PCG systems, algorithms, and
techniques. But for PCG to be absorbed into the practice of
game design, it must be contextualised within design-centric
as opposed to AI or engineering perspectives. We therefore
provide a set of design metaphors for understanding potential
relationships between a designer and PCG. These metaphors
are: TOOL, MATERIAL, DESIGNER, and DOMAIN EXPERT.
By examining PCG through these metaphors, we gain the
ability to articulate qualities, consequences, affordances, and
limitations of existing PCG approaches in relation to design.
These metaphors are intended both to aid designers in understanding
and appropriating PCG for their own contexts, and
to advance PCG research by highlighting the assumptions implicit
in existing systems and discourse"
Abstract: "Search-based procedural content generation methods allow
video games to introduce new content continually, thereby
engaging the player for a longer time while reducing the burden
on developers. However, games so far have not explored
the potential economic value of unique evolved artifacts.
Building on this insight, this paper presents for the first time a
Facebook game called Petalz in which players can share flowers
they breed themselves with other players through a global
marketplace. In particular, the market in this social game allows
players to set the price of their evolved aestheticallypleasing
flowers in virtual currency. Furthermore, the transaction
in which one player buys seeds from another creates
a new social element that links the players in the transaction.
The combination of unique user-generated content and social
gaming in Petalz facilitates meaningful collaboration between
users, positively influences the dynamics of the game,
and opens new possibilities in digital entertainment."
Abstract: "We define data games as games where gameplay and/or
game content is based on real-world data external to the
game, and where gameplay supports the exploration of and
learning from this data. This concept is discussed in rela-
tion to open data, procedural content generation and serious
games, and research challenges are outlined. To illustrate
the concept, we present six prototype games and content
generators of our own making. We also present a tentative
taxonomy of actual and potential data games, and situate
the described games within this taxonomy."
Abstract: "This paper discusses how to use design patterns in procedural
level generation, with particular reference to the classic
console game Super Mario Bros. In a previous paper, we analyzed
the levels in this game to nd a set of recurring level
design patterns, and discussed an implementation where levels
were produced from concatenation of these patterns. In
this paper, we instead propose using patterns as design objectives.
An implementation of this based on evolutionary
computation is presented. In this implementation, levels are
represented as a set of vertical slices from the original game,
and the tness function count the number of patterns found.
Qualitative analysis of generated levels is performed in order
to identify strengths and challenges of this method"
From the abstract: "In this paper, we propose
the integration of two PCG-based approaches (experiencedriven
and context-driven PCG) to support the generation
of adaptive mobile game levels. We present and discuss the
implementation of our approach in an existing game, 7's
Wild Ride. Gameplay semantics and player modeling are
used to steer a level generator, featuring a time-dependent
dynamic diculty adjustment mechanism. From our two
user studies, we conclude that (i) context-driven levels are
preferable over traditional ones, and (ii) the game can adapt
to dierent player types, keeping its gameplay balanced and
player satisfaction."
Abstract: "In this project, the following research question is set forth: is it possible to create fair
maps for a video game using multi objective evolution algorithms? A description of
the video game used for this project, Civilization V, is provided as well as an overview
of other map generation methods, and research being done in the field of procedural
content generation. A definition for what is fair is made and expressed through
functions, that evaluate maps for the video game Civilization V. These evaluation
functions express five distinct perspectives on how fair maps are perceived. The
fitness functions are designed to conflict as little as possible with each other. A
method is defined as to how this theory is applied in practice to generate maps for
Civilization V. The evaluation functions are applied on maps from the game's map
generation method, and compared to maps that have been evolved with the method
provided by this project."
Abstract: "Procedural generation of games has become an active re-
search eld. We present a system that automatically gen-
erates an interactive ction (IF) by learning from crowd-
sourced corpora of example stories. We ask crowd workers
from Amazon Mechanical Turk to write short stories about
a given situation with simple language, from which a plot
graph is learned, containing plot events, temporal prece-
dence and mutual exclusion relations between the events.
The plot graph describes an IF where players and non-player
characters choose from executable events as determined by
the plot graph. We demonstrate an IF learned from the
domain of bank robbery"
Abstract: "We introduce Mechanic Miner, an evolutionary system for
discovering simple two-state game mechanics for puzzle platform games.
We demonstrate how a re
ection-driven generation technique can use a
simulation of gameplay to select good mechanics, and how the simulation-
driven process can be inverted to produce challenging levels specic to a
generated mechanic. We give examples of levels and mechanics generated
by the system, summarise a small pilot study conducted with example
levels and mechanics, and point to further applications of the technique,
including applications to automated game design."
Abstract: "In this paper, we describe a methodology for capturing player
experience while interacting with a game and we present a data-driven
approach for modeling this interaction. We believe the best way to adapt
games to a specic player is to use quantitative models of player ex-
perience derived from the in-game interaction. Therefore, we rely on
crowd-sourced data collected about game context, players behavior and
players self-reports of dierent aective states. Based on this informa-
tion, we construct estimators of player experience using neuroevolution-
ary preference learning. We present the experimental setup and the re-
sults obtained from a recent case study where accurate estimators were
constructed based on information collected from players playing a rst-
person shooter game. The framework presented is part of a bigger picture
where the generated models are utilized to tailor content generation to
particular player's needs and playing characteristics."
Abstract: "Abstract-In computer games and simulations, content is often
rather static and rigid. As a result, its prescripted nature can lead
to predictable and impersonal gameplay, while alienating unconventional
players. Adaptivity in games has therefore been recently
proposed to overcome these shortcomings and make games more
challenging and appealing. In this paper, we survey present research
on game adaptivity, identifying, and discussing the main
challenges, and pointing out some of the most promising directions
ahead.We first survey the purposes of adaptivity, as the principles
that could steer an adaptation and generation engine. From this
perspective, we proceed to thoroughly discuss adaptivity's targets
and methods. Current advances and successes in this emerging
field point to many yet unexplored research opportunities. Among
them, we discuss the use of gameplay expectations, learning preferences,
and assessment data in the integrated adaptation of game
worlds, scenarios, and quests. We conclude that, among other
methods, procedural content generation and semantic modeling
can powerfully combine to create offline customized content and
online adjustments to game worlds, scenarios, and quests. These
and other promising methods, deserving ample research efforts,
can therefore, be expected to significantly contribute towards
making games and simulations even more unpredictable, effective,
and fun."
From the abstract: "Traditionally, the tasks associated with
game AI revolved around non player character (NPC) behavior
at dierent levels of control, varying from navigation
and pathnding to decision making. Commercial-standard
games developed over the last 15 years and current game
productions, however, suggest that the traditional challenges
of game AI have been well addressed via the use of sophisticated
AI approaches, not necessarily following or inspired
by advances in academic practices. The marginal penetration
of traditional academic game AI methods in industrial
productions has been mainly due to the lack of constructive
communication between academia and industry in the
early days of academic game AI, and the inability of academic
game AI to propose methods that would signicantly
advance existing development processes or provide scalable
solutions to real world problems. Recently, however, there
has been a shift of research focus as the current plethora
of AI uses in games is breaking the non-player character AI
tradition. A number of those alternative AI uses have already
shown a signicant potential for the design of better
games.
This paper presents four key game AI research areas that
are currently reshaping the research roadmap in the game
AI eld and evidently put the game AI term under a new
perspective. These game AI
agship research areas include
the computational modeling of player experience, the procedural
generation of content, the mining of player data on
massive-scale and the alternative AI research foci for enhancing
NPC capabilities."