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Procedural content generation: goals, challenges and actionable steps

Procedural Content Generation: Goals,
Challenges and Actionable Steps
Julian Togelius1, Alex J. Champandard2, Pier Luca Lanzi3,
Michael Mateas4, Ana Paiva5, Mike Preuss6, and
Kenneth O. Stanley7
1 Center for Computer Games Research, IT University of Copenhagen,
Copenhagen, Denmark
2 KG, Vienna, Austria
3 Department of Electronics and Information, Politecnico di Milano, Milano,
4 Center for Games and Playable Media, University of California, Santa Cruz,
California, USA
5 Intelligent Agents and Synthetic Characters Group, INESC-ID, Lisboa,
6 Department of Computer Science, Technical University of Dortmund,
Dortmund, Germany
7 Department of Electrical Engineering and Computer Science, University of
Central Florida, Orlando, Florida, USA
This chapter discusses the challenges and opportunities of procedural content generation (PCG)
in games. It starts with defining three grand goals of PCG, namely multi-level multi-content
PCG, PCG-based game design and generating complete games. The way these goals are defined,
they are not feasible with current technology. Therefore we identify nine challenges for PCG
research. Work towards meeting these challenges is likely to take us closer to realising the three
grand goals. In order to help researchers get started, we also identify five actionable steps, which
PCG researchers could get started working on immediately.
1998 ACM Subject Classification I.2.1 Applications and Expert Systems: Games
Keywords and phrases procedural content generation, video games
Digital Object Identifier 10.4230/DFU.Vol6.12191.61
1 Introduction
Procedural content generation (PCG) refers to the algorithmic generation of game content
with limited or no human contribution. “Game content” is here understood widely as
including e.g. levels, maps, quests, textures, characters, vegetation, rules, dynamics and
structures, but not the game engine itself nor the behaviour of NPCs. PCG has been part of
published games since the early eighties, with landmark early examples being the runtime
©Julian Togelius, Alex J. Champandard, Pier Luca Lanzi, Michael Mateas, Ana Paiva, Mike Preuss,
and Kenneth O. Stanley;
licensed under Creative Commons License CC-BY
Artificial and Computational Intelligence in Games. Dagstuhl Follow-Ups, Volume 6, ISBN 978-3-939897-62-0.
Editors: Simon M. Lucas, Michael Mateas, Mike Preuss, Pieter Spronck, and Julian Togelius; pp. 61–75
Dagstuhl Publishing
Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Germany
62 Procedural Content Generation: Goals, Challenges and Actionable Steps
generation of dungeons in Rogue and the extremely compressed representation of hundreds of
star systems in Elite. Prominent recent examples of PCG include the ubiquitous SpeedTree
system, used for generation of trees, grass and other types of vegetation in hundreds of
commercial games, and the generation of dungeons, weapons and items in the Diablo series
of games. PCG can be used for a variety of reasons, including providing variety, reducing
development time and development costs, saving space in transmission or on disk, augmenting
human creativity and enabling adaptivity in games.
The academic research community around PCG has formed only within the last few
years. While a number of important papers were published in venues dedicated to AI or
graphics research, the first workshop devoted entirely to PCG took place in 2009 and the
first journal special issue devoted to the topic came out only in 2011
. The solutions to
content generation problems that have been explored by academic researchers have tended to
be focused more on adaptable and controllable (and sometimes highly complex) algorithms,
whereas the types of algorithms that have so far been used in published games tend to be
simpler, faster and less controllable.
The workgroup on PCG decided to look at the field from the perspective of what research
would be most important to do in the future in order to ensure that real breakthroughs
are made, and modern PCG techniques will be able to add value to the games industry by
enabling new types of games as well as new ways of developing games. We took a top-down
perspective and started with thinking about what sort of things we would ultimately want
PCG to be able to achieve; the grand goals of the research field. We then tried to identify
the most important challenges that need to be overcome in order to be able to realise these
goals, and finally identified a handful of actionable steps that could make progress towards
overcoming these challenges, and which could be taken already today by someone – perhaps
you? – interested in contributing to the field.
2 Goals
The following is a list of what we consider the most important goals of PCG research. In each
case, the goal is currently not obtainable and it would require significant further research
effort leading to some sort of breakthrough in order to be able to realise the goal. However,
in each case there is already some work done that directly addresses this goal, so for each
item we list what we consider the most relevant previous work.
2.1 Multi-level Multi-content PCG
Imagine being able to push a button and generating a complete game world: terrain,
vegetation, roads, cities, people, creatures, quests, lore, dialogue, items, vehicles; polygons,
graphs, textures, text. For example of such a fully fledged game world, think of the Elder
Scrolls V: Skyrim (Bethesda) game world, complete with all that which makes it so immersive
and exciting – except for the underlying game engine. And then press the same button again,
and you get a fresh new world, different from the previous world in every respect: the details,
the overall structure, the look and feel. Maybe it’s a sci-fi world threatened by invasion, or a
murder mystery in a contemporary industrial city. The only limits for the expressive space
The PCG workshop runs annually since 2010, co-located with the Foundations of Digital Games
Conference. The autumn 2011 issue of IEEE Transaction on Computational Intelligence and AI in
Games is entirely devoted to PCG. A discussion group for the PCG community can be found at
J. Togelius et al. 63
are the capabilities of the underlying game engine, which provides primitives for movement,
social interactions, combat, scoring etc. The system we imagine should be so flexible so that
it would be possible to plug in a new game engine, complete with a new set of rules and
dynamics, and go on to generate complete game worlds that fit with that engine.
In other words we are here envisioning a system that can generate multiple types of
quality content at multiple levels of granularity in a coherent fashion while taking game
design constraints into consideration. Nothing like this exists yet, and while it might not be
possible to achieve such a system in the foreseeable future, we see much room for progress in
this direction.
Almost all existing approaches to content generation generate a single type of content
for a single game, and the results all too often look uninspired and generic. There are some
interesting examples of trying to generate several types of content so that they fit in with
each other. Dwarf Fortress (Bay 12 Games) generates many aspects of the game world,
including its geology and backstory, all the way down to the socks on each foot of individual
dwarfs. However, for each type of content the generation process is very simple, and the
generated game worlds show little variation. A similar problem is tackled by Hartsook et
al., though that paper employs a “waterfall” model where one type of content (stories) are
generated first and the second (maps) afterwards, with no feedback between process [
Another approach to multi-level multi-content generation is that of the Sketchaworld system
by Smelik et al. [
]. This system allows for mixed initiative generation of various aspects
of a landscape, including topology, vegetation, and the placement of roads and buildings.
All objects have semantics and defined interdependencies, which together with a conflict
resolution system allows editing on one level (e.g. changing the flow of a river) to have effects
on entities on other levels (e.g. a bridge is automatically created over the river to allow a
pre-existing road to pass through). However, that system does not address game-specific
design considerations such as balance or challenge.
2.2 PCG-based Game Design
Imagine a game where procedural content generation was a central mechanic, without which
the game could not exist at all; in fact, the whole genre to which the game belongs could not
exist without procedural content generation. Imagine that the game was truly endless, and
that exploration of an infinite range of content was a central part of the gameplay, indeed
the main reason the game was so appealing.
Almost all existing approaches to PCG focus on generating content for an existing game,
where the core game itself could exist without the PCG mechanism. PCG is done to facilitate
development, or to allow for runtime adaptation. Even in games such as Rogue,Spelunky
and Diablo, where a key feature of the game is the endless variation in game content, all of
the levels could in principle have been generated offline and presented to the player without
taking player choice into account. Creating games where a PCG algorithm is an essential
part of the game design requires innovations both in game design, where parameters to
the PCG algorithm need to be meaningfully based on player actions, and in PCG, where
the algorithm needs to be reliable and controllable beyond the capacities of most current
A few games have made progress towards realising this vision. Galactic Arms Races
and Petalz both feature the evolution of new content as a core part of the game, where the
players respectively evolve weapons to defeat enemies or evolve flowers to impress friends
as the game is being played [
]. Endless web is a platform game that lets the player
explore different dimensions of content space, and generates new levels in response to the
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64 Procedural Content Generation: Goals, Challenges and Actionable Steps
player’s actions[
]. In Infinite Tower Defence, the role of PCG is to create new levels and
enemies that match the current strategy of the player so as to force the player to explore
new strategies [
]. Another example is Inside a Star-filled Sky, which features a “zooming”
mechanic, where the player character can enter enemies and explore new levels inside them;
this yields an apparently endless hierarchy of nested levels. While these games are addressing
the challenge of PCG-based game design, they are still variations on well-known genres rather
than examples of genres that could only exist because of PCG.
2.3 Generating Complete Games
Imagine a PCG system that could create complete games. Not just content for an existing
game, but the whole game from scratch, including the rules and game engine. At the press of
a button, starting from nothing, the system will create a game no-one has played before and
which is actually enjoyable to play. This would involve automating the arguably most central
and “AI-complete” aspects of game design, including estimating how a human player would
experience interacting with a complete system of rules and affordances. Perhaps the system
accepts parameters in the form of a design specification for the game. For the example, a
human might task the system with designing a game that features fog of war, that promotes
collaboration or that teaches multiplication.
Several attempts have been made to generate game rules, sometimes in combination
with other aspects of the game such as the board. Of particular note is Cameron Browne’s
Ludi system which generated a board game of sufficient novelty to be sold as a boxed
product through searching through a strictly constrained space of board games [
]. This
system built on evolutionary computation, as did earlier [
] and later [
] attempts to evolve
simple arcade-style games. Other attempts have build on symbolic techniques such as logic
programming [
]. While these examples have proven that generation of playable game
rules is at all possible, they all generate only simple games of limited novelty.
3 Challenges
Analysing what would be needed in order to reach the grand goals discussed above, the
workgroup arrived at a list of eight research challenges for procedural content generation.
Successfully meeting any of these challenges would advance the state of the art in PCG
significantly, and meeting all of them would probably render the goals described above
attainable. Addressing any of these challenges could make a good topic for a PhD thesis.
3.1 Non-generic, Original Content
Generated content generally looks generic. For example, looking at the dungeons generated
for a roguelike game such as those in the Diablo series, you easily get the sense that this is
just a bunch of building blocks hastily thrown together with little finesse – which is just
what it is. Re-generate the level and you get a superficially very different but ultimately
equally bland level. Most generated levels lack meaningful macro-structure and a sense of
progression and purpose. Very rarely would you see a generated level about which you could
say that it was evidence of skill or artfulness in its creator, and even more rarely one which
showcases genuine design innovation. Compare this with the often masterfully designed levels
in comparable games such as those in the Zelda franchise. The Zelda levels are aesthetically
pleasing in several ways, providing a clear sense of place and progression, and often offering
some original and unique take on the design problems of action adventure games.
J. Togelius et al. 65
There are a few examples of PCG systems having come up with what could be called
genuine inventions. In the Galactic Arms Race game, several of the weapons that were
generated (such as the tunnel-maker and hurricane) were surprising to players and designer
alike and unlike anything the designers had seen before, yet were effective in the game and
opened up for new playing styles, as if they had been designed by a human designer [
]. As
discussed above, Browne’s Ludi system managed to come up with a game (Yavalath) that
was sufficiently novel to be sold as a boxed product. However, we do not know of a system
that has exhibited sustained creativity, or that (in the language of Margaret Boden [
]) has
displayed transformational rather than just exploratory creativity.
The challenge, then, is to create content generators that can generate content that is
purposeful, coherent, original and creative. This challenge is quite broad, as interpretations
of these adjectives could vary – this only means that there are many different ways of
approaching the challenge.
3.2 Representing Style
Directly connected to the previous challenge but somewhat more specific is the challenge to
create a content generator that can create content in a particular style that it has somehow
learned or inferred. Human artists of various kinds can observe artefacts produced by another
artist, and learn to imitate the style of that artist when producing new artefacts – e.g., a
skilful painter could study a number of Picasso paintings and then produce new paintings that
were recognisably in the same style (while presumably not exhibiting the same creativity),
and then go to on to study Mondrian paintings and produce Mondrian-like paintings of her
own. An analogous capacity in PCG could be a level generation system that could study
the seminal level designs of Shigeru Miyamoto in the Zelda and Super Mario series, and
produce similar designs automatically; the same generator could, after being presented with
John Romero’s significantly different designs for Doom levels, learn to imitate that style
too. Perhaps the generator could be presented with artefacts that were not game levels,
for example architectural designs by Frank Lloyd Wright, and learn to reproduce that style
within the constrained design space of levels for a particular game. Similarly competent
texture generators, game rule generators and and character generators could also be imagined.
It is important to note that the challenge is not only to imitate the surface properties of a
set of designs (such as recurring colours and ornamentation) but also the deeper features of
the design, having to do with expression of ideas and emotions in interplay with the player
and game design.
Some attempts have been made to model player preferences in level generators [
or designer preferences in interactive evolution for content generation [
]. However, the
preferences modelled in these experiments are still very indirect, and the generated artefacts
still exhibit the style of the generator more than that of the player or designer.
3.3 General Content Generators
Almost all existing PCG algorithms generate a single type of content for a single game.
Reusability of developed PCG systems is very limited; there is no plug-and-play content
generation available. This can be contrasted with the situation for other types of game
technology, where game engines are regularly reused between games and even some aspects of
game AI (such as pathfinding) now being available as middleware. The only PCG middleware
that is actually in use in multiple games is SpeedTree, but this again generates only a single
type of content (vegetation) and that type is of little functional significance in most games,
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66 Procedural Content Generation: Goals, Challenges and Actionable Steps
meaning that the risks of generating content are rather low; ugly shrubbery is ugly but tends
not to break the level. The lack of readily available PCG systems that could be used without
further development to generate for example levels or characters for a new game is probably
holding back adoption of PCG techniques in the game industry.
It is already the case that certain techniques underly a number of different PCG sys-
tems. For example, L-systems [
] are at the core of both SpeedTree and techniques for
creating landscapes [
] and levels [
]. Similarly, compositional pattern-producing networks
(CPPNs) [
] are the basis for both the spaceships in Galactic Arms Race [
], the flowers in
Petalz [
] and the pictures in PicBreeder [
]. However, in each case significant engineering
effort was required to make the method work with the particular type of content in the
particular game.
A general content generator would be able to generate multiple types of content for
multiple games. The specific demands, in term of aesthetics and/or in-game functionality, of
the content should be specified as parameters to the generator. A game designer armed with
such a tool would just need to properly specify the requirements for the content that should
be part of a new game in order to promptly have a means of automatically generating content
for it. One idea for how to achieve this is to treat PCG algorithms as content themselves,
and generate them using other PCG methods so as to fit the particular content domain or
game they are meant to be applied to [14].
3.4 Search Space Construction
If content is to be generated then it must be be situated within a search space. The structure
of the search space determines what content can be reached from small perturbations of any
particular instance in the search space. For a content generator to be successful, the structure
of the search space needs to have certain forms of locality. In general, small perturbations
in the underlying representation should not lead to radical changes in the appearance or
functionality of the content itself. For example, a table should not turn into a mushroom in
a single small mutation. Rather, the table might become a little shorter, a little taller, or a
little rounder, but it would remain a recognisable table.
This search space structure is ultimately determined by the selected underlying rep-
resentation for the class of content being generated. Existing representations include the
L-sytems [
] and CPPNs [
] discussed in the previous section, as well as logic-based
representations such AnsProlog code snippets [
] and more direct representations where
individual numbers correspond to individual features of the artefact. Such representations
bias the generator towards producing certain qualitative themes, such as fractals in L-systems
or symmetry in CPPNs. These biases are a reflection of the structure of the search space
induced by the representations – fractals tend to be reachable from many different parts of a
search space induced by L-systems. Thus the representation becomes an implicit means of
structuring which types of content neighbour which, and therefore which artefacts can lead
to which others.
This relationship between representation and search space structure highlights the signi-
ficant challenge of designing a generator for a specific type of content: If a type of content
is to be generated – say vehicles or buildings – then the engineers designing the generator
must carefully construct a representation that induces a reasonable structure on the search
space. One would not want airplanes to change in one step into wheelbarrows. To ensure
such a smooth a tightly coupled landscape, the designer must intimately understand the
relationship between the underlying representation and the structure of the space it induces,
a relation which is not necessarily intuitive. For this reason, designing the search space to
J. Togelius et al. 67
make a satisfying generator requires significant skill and insight, and there are currently few
general principles known to the research community for undertaking such a task.
In the future, it is possible that tools can be built to aid in adapting a particular
representation for a particular class of content. For example, as noted in the previous section,
CPPNs have encoded pictures, weapon systems, and flowers by interpreting their outputs
and choosing their inputs carefully. It would be most helpful if a tool could help to automate
such choices so that a particular representation could be adapted to a particular content
class more easily.
3.5 Interfaces and Controllability for PCG Systems
Most existing PCG systems are not easy for a human to interface with and control. Many
classic PCG implementations, for example the dungeon generators in many roguelike games,
have no parameters of control at all; taking a random seed as input, a level is generated as
output. In very many cases, you as a user (or as a game) would need to have more control of
the generated artefact. Like controlling how difficult a level is, whether it should be more
suited to speed runners or to explorer-type players, how much treasure and how many puzzles
it should contain, and whether it should include a green flagpole at a particular location
or perhaps five pixels to the left of that position. Or the age of a generated flower, the
intuitiveness of a ruleset or the hipness of a car. There are many possible types of control
that could be desirable, depending on the game and the designer.
Some classic constructive algorithms such as L-systems offer ways for the designer to
specify aspects of the generated content, such as the “bushiness” of a plant. Search-based
approaches allow the designer to specify desirable properties of the content in the form
of objectives, but encoding the desired qualities in a fitness function is often far from
straightforward and there is no guarantee that content with high values on these objectives
can be found in the search space. Other PCG paradigms such as solver-based PCG using e.g.
Answer Set Programming [
] offer complementary ways of specifying objectives, but again,
it is not easy to encode the desired qualities for a non-expert. The mixed-initiative PCG
systems Sketchaworld [
] and Tanagra [
] explicitly address this problem by allowing the
user to interact with the PCG system by moving and creating objects in physical space, and
thus imposing constraints on how what can be generated where. These systems clearly show
a viable way forward, but so far only some aspects of control has been achieved (physical
location) at the cost of some limitations in what sort of underlying PCG methods can be
What would it mean to allow users (be they designers or players, or perhaps some other
algorithm such as a challenge balancing system) complete control over the content generation
algorithm? Presumably it would mean that they could at any point during the generation
process change any aspect of the content: making the level more blue or less scary or just
making all of the gaps except the fifth one contain apples. Then the generator responds by
implementing the changes, perhaps introducing new features or removing others, but still
respecting what the user has specified wherever possible, and intelligently resolving conflict
between specifications (e.g. the apples in the gaps could make the level more difficult). One
could imagine something like Adobe Photoshop’s extreme array of expressive tools, including
brushes, filters and abilities to only have modifications apply to particular layers, but all
the way taking game design into account and autonomously generating appropriate content
suggestions. It should be emphasised that this is not only a formidable challenge for PCG
algorithms, but also for human-computer interaction. It is not even obvious how to represent
aspects of game content that depend on being played to be experienced (such as game rules)
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68 Procedural Content Generation: Goals, Challenges and Actionable Steps
in an editor; a recent attempt at mixed-initiative generation of game rules mostly highlighted
the problem [30].
3.6 Interaction and Opportunistic Control Flow Between Generators
Closely related to the previous challenge, and crucial for the goals of multi-level PCG and
generating complete games, is the challenge to devise workable methods for communication
and collaboration between algorithms working on generating different aspects or layers of
the same artefact. For example, when a system generates the rules for a game, the physical
environments for the same game and the characters or creatures that feature in it, the various
generative algorithms must be able to communicate with each other. The simplest way of
implementing this would probably be a “waterfall” model where the rules are generated
first, positing requirements on the terrain/levels generator, in turn further constraining the
space available for the creature/character generators. But this rules out any innovations
in rules which are dependent on, and initiated by, uncommon solutions and crazy ideas in
level or character design. In fact, as the rule generator cannot know what sort of characters
the character generator will be able to produce (unless the latter’s search space is severely
constrained), the rules will have to be constrained to be very bland and workable with pretty
much any characters. For these reasons, games developed by teams of humans are often
developed in a much more opportunistic way, where opportunities or problems discovered at
any content layer could affect the design of the other layers (e.g. the invention of a new type
of enemy spurs the invention of new rules).
How can we replicate such an opportunistic control flow in a completely (or mostly)
algorithmic environments, where algorithms (or perhaps some algorithms and some humans)
collaborate with each other? One could imagine a system where constraints are posted in
a global space, but this requires that a language and/or ontology be constructed to make
such constraints comprehensible across generators, and also that a system is devised for
working out priorities and solving conflicts between constraints. Going further, one could
imagine equipping the content generators with models of themselves so as to provide a level
of introspection, allowing them to exchange models of the bounds of their generative spaces.
3.7 Overcoming the Animation Bottleneck
In modern 3D computer games, animation is a major concern. Almost everything needs
to be animated: creatures, characters, vehicles and features of the natural world such as
vegetation and water. Creating believable animations even if the underlying characters are
not procedurally generated is a huge challenge. In particular:
Motion capture or hand animation is very expensive to acquire, and requires either the
use of specialised motion capture facilities or an extensive team of animators.
Data-heavy forms of animation such as motion capture or hand-animation also costs a
significant amount of time, and are often a bottleneck for improving character behaviour.
Animation systems based on data require significant runtime overheads for shifting around
the data, decompressing it and generating runtime poses via blending.
This makes current animation techniques a bottleneck in three different ways, each as
important as the other. Procedural techniques are already starting to resolve each of these
three different issues, and the games industry is highly interested in the results [5].
There are many domain-specific problems to generating compelling animations for char-
acters that were hand defined, but harder still is the problem of animating procedurally
J. Togelius et al. 69
generated creatures. Being able to generate an artefact does not mean that one automatically
is able to animate it, and if one is not able to convincingly animate an artefact it is more or
less useless in-game, as it would break the suspension of disbelief.
The big challenge of procedural animation is to match the high expectations of human
observers, without having to resort to stylisation as a solution. This solution will involve
subtle combinations of data and code that are crafted and assembled together masterfully by
skilled technical animators, and new algorithms to make this possible.
3.8 Integrating Music and Other Types of Content
While most computer games feature music, the whole audio component usually only serves
the task of supporting the game flow or emphasising the general atmosphere of the game.
This is often done either by producing complete sound tracks as in the movie industry or
by designing a very simple generative system (as in the Google app Entanglement) that
repeatedly recombines single parts in order to stretch the available music and prevent boredom.
Games that actively use the music as source of information to create game content or, vice
versa, use game events for adjusting or even creating music are still rare.
In many well-known games based on music (e.g. Guitar Hero or SingStar), the interaction
between music and game events is completely fixed and has been manually created. An
overview of the different possibilities is given in [
]. Some examples of more complex
interaction are:
Rez (Sega)
from 2001, a rail shooter that tightly binds the visual impression (and
appearance of enemies) to the composed music and also feedbacks user actions acoustically.
Electroplankton by Nintendo
from 2005, where the player interacts in various ways with
the game to create audio and visual experiences.
The turntable-like game Scratch-Off [
] that requires user interaction matching the rhythm
of the music while blending over to another piece of music.
The Impossible Game is a graphically simple but challenging platform game for consoles
and mobile phones that requires the user to cope with very different obstacles that are
generated in accordance with current music events.
The Bit.Trip series by Gaijin Games features the main character Commander Video who
has to cope with game events mostly based on the rhythm of the played music in 6 very
different games (rhythm shooter, platformer).
Wii Music automatically generates melodies based on how the player moves the controller.
BeatTheBeat [
] features three mini-games (rhythm-based tap game, shooter, and tower
defence) that each use the available music as background and its analysed feature events
as source for game event creation.
Music informatics has made great strides in recent years, including the emergence
personalised music selection systems (including learning personal preferences) as well as
complex music generation systems. Given this progress, it should be possible to establish
game/music systems that interact in a much more complex way than what we are used to
see in most games. In principle, one could take an existing simple game and attach it to a
recommendation or generating system. Some thought has to be put into designing clever
interfaces, but the problem appears per se as solvable. Using existing music as input source
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70 Procedural Content Generation: Goals, Challenges and Actionable Steps
for realtime production of game content just becomes possible now as modern computers are
powerful enough to analyse the music online. This scheme can enhance a game considerably
because the user can modify it by selecting the music that best reflects the current mood or
just some very different music to create a new experience.
However, this strategy is only applicable for simple games with a restricted number of
(partly repetitive) game components. For more complex games, interaction in the other
direction (game events influence the music played) may make more sense. The components
to achieve this are available, simple forms of modulation would be changing volume, speed,
or key of the music. One could also think of online remixing by changing the volume of single
instrument tracks, an approach that has been tried some years ago by several musicians by
means of online tools (e.g. Peter Gabriel, William Orbit), but not in the games context.
The feasibility of this approach highly depends on access to the single music and games
components, but technically appears to be rather simple. A more sophisticated approach
would apply a music generating system in order to modify or re-create the currently played
music. However, for achieving a believable connection between game events and music,
the semantic of the game events needs to be defined in a transferable fashion, for example
through their effects on the player’s mood.
3.9 Theory and Taxonomy of PCG Systems
While PCG research has been steadily increasing in volume in the last few years, there has
been a lack of unifying theory to guide the research or even to help relating the disparate
contributions to each other. New algorithms have been proposed and case studies carried
out in a number of game domains, but it is seldom clear in what ways a new approach is
better (or worse, or just different) to existing approaches. A theory and taxonomy of PCG
would explain the relative advantages of different approaches, why some content generation
problems are harder than others, and which approaches are likely to work on what problems.
It would also help situate and analyse any new algorithms proposed. A recent paper makes an
initial attempt to provide a taxonomy for PCG, but covers mostly search-based approaches,
in particular those based on evolutionary computation [33].
4 Actionable Steps
The challenges listed above are somewhat abstract and are mostly long-term projects. For
some of them, it is not even clear how to approach them. To make matters more concrete,
and to provide a set of example projects for anyone wishing to contribute to advancing
the state of the art of procedural content generation, we devised a number of actionable
steps. These are more specific research questions around which projects with a limited scope
could be formulated, and for which the technical prerequisites (in terms of algorithms and
benchmarks) already exist. You could start working on any of these steps already today.
4.1 Atari 2600 Games
Inventing a system that could generate a complete game with the complexity and scope of
a modern AAA game might be a tall task – after all, developing these games often takes
hundreds of person-years. Fortunately, not all games are equally complex. Those games
that were made for early computers and game consoles were substantially less complex, as
available memory size, processing speed, graphics resolution and development teams were
all fractions of what they are today. The Atari 2600 games console, released in 1977, had
J. Togelius et al. 71
4 kilobytes of RAM, a 1.2 MHz processor and was home to classic games such as Pitfall,
Breakout,Adventure and Pac-Man. All of them had two-dimensional graphics that adhered
to certain constraints regarding e.g. the number of movable objects that were dictated by
the system’s hardware design.
One could realistically try to directly approach the third of the grand goals outlined
above, that of generating complete games, working within the much constrained space of
games that would be possible on a system such as the Atari 2600. The limited space makes
it much more tractable to search for good games, regardless of which search-mechanism
would be used. The limited computational requirements of running these games would also
make it possible to test them, for example by simulating playthroughs, at a much faster pace
than real-time. A successful game generation system for Atari 2600 games should be able to
re-invent classic games such as Breakout and Pac-Man, but also to invent enjoyable games
that have never before been seen and which differ in some interesting way from all existing
games. Developing such a system would require devising a description language for this type
of video games, that is complete enough to cover essentially all interesting such games, but
which still enables the space to be effectively searched; for some thoughts on how this could
be done, please see the chapter Towards a Video Game Description Language in this volume.
Relates directly to challenges: general content generators, search space construction,
interaction and opportunistic control flow.
4.2 Procedural Animation for Generated Creatures
One way of approaching the challenge of bringing PCG and procedural animation together is
to develop a creature generator which generates creatures together with suitable procedural
animation. The most impressive attempt to do something similar is probably the Creature
Creator in Spore, but that is an editor rather than an automatic content generator, and
imposes certain constraints on the generated creatures that should be avoided in order to be
able to search a more complete creature space.
Relates directly to challenges: overcoming the animation bottleneck, interfaces for PCG
4.3 Quests and Maps
The computational generation and adaptation of narrative is a specific type of PCG which
enjoys its own research field, and a number of promising approaches have been presented,
most of them based on planning algorithms [
]. Another domain of PCG which has seen
much attention is the generation of maps of different kinds. Maps are automatically generated
in games such as Civilization and Diablo, and a number of recent studies have investigated
techniques such as cellular automata [
], grammars [
] and evolutionary computation [
for generating interesting and/or balanced maps for different types of games.
Generating maps and quests together could potentially be an excellent showcase for
multilevel PCG (the first of the grand goals outlined above), as the best-designed games
often feature quests and maps that interact and reinforce each other in clever ways – the
map helping the game tell the story, and the story helping the player explore the map –
and there are workable approaches to generating each type of content separately. However,
there is surprisingly little work done on generating quests and maps together. One of the
few examples is Dorman’s use of grammars for generating Zelda-style dungeons and quests
together, achieving good results by severely limiting the domain [
]. Another is Hartsook et
Chapter 05
72 Procedural Content Generation: Goals, Challenges and Actionable Steps
al.’s generation of matching maps and quests, by simply generating the map after the quest
so that the former fits with the latter [9].
There is plenty of scope for taking this further and generating maps and quests that are
perfect fits for each other. The interested investigator could start by taking one of those
algorithms that has been proven to work well for one domain (maps or quests) and try to
encode the other domain within the first, or by devising a scheme where map and quest
generators take turns to respond to each other, or perhaps by trying to invent a completely
new algorithm for this task. One could also try to allow human intervention and input at
any phase of the quest/map generation.
Relates directly to challenges: interaction and opportunistic control flow, general content
generators, interfaces for PCG systems.
4.4 Competent Mario Levels
The Mario AI Benchmark is a popular testbed within the CI/AI in games community, based
on an open source clone of Nintendo’s classic platformer Super Mario Bros. The benchmark
has been used for a series of competitions focused on developing AI controllers that play
the game proficiently [
], but in 2010 and 2012 it was also used for a competition where
entrants submitted level generators capable of generating personalised levels for particular
players [22]. A number of PCG systems were submitted to this competition, and a number
of other PCG experiments using the Mario AI Benchmark have been published following the
competition [21, 28].
However, comparing the quality of the generated levels with those that can be found in
the real Super Mario Bros game, or with human-designed levels in any other high-quality
platformer, makes any PCG aficionado disappointed. The generated levels typically lack a
sense of progression, or any other macro-structure for that matter. Unlike the real Super
Mario Bros levels, there is no sense that they are telling a story, trying to teach the player a
skill, or hiding a surprise. Furthermore, the generated levels frequently feature items and
structures that make no sense, unexplainable difficulty spikes and repeated structures that
seem to be taken straight from a structure library. A high priority for someone interested
in procedurally generating platform levels should be to devise an algorithm that can create
levels with a sense of purpose. Using the Mario AI Benchmark as a testbed means that there
is no shortage of material for comparisons, both in the form of level generators and in the
form of professionally designed levels.
Relates directly to challenges: non-generic content, representing style.
4.5 Player-directed Generation with Model-based Selection
A final intriguing possibility is that player-directed generation in the style of Galactic Arms
Race [
] could be enhanced by combining it with model-based selection such as in [
]. In a
game like Galactic Arms Race, the game generates new content based on content players
have liked in the past (as evidenced by e.g. using it). This idea works to ensure that new
content appearing in the world derives from content that players appreciated.
However, as the number of players climbs higher, the amount of content generated will
also climb because player behaviour generally leads to new content spawning in the world.
With a relatively small population of players, this dynamic poses few problems because
the probability of any player in the game eventually experiencing a reasonable sampling
of the diversity of generated content is high. However, with many players, the consequent
content explosion means that most players will see only a small fraction of the diversity of
J. Togelius et al. 73
content that the game is able to produce. In that situation, the question arises whether
the overall search for content might benefit from trying to expose players to content that
they are likely to find interesting. That is, the game might try to model the type of content
that individual players prefer and thereby avoid wasting effort presenting newly-generated
instances to players who are unlikely to be interested in them. If such mismatches occur on
a large scale, then pockets of the search space that could have been explored fruitfully might
be abandoned simply because the players who would have been interested in such content
never had to the opportunity to observe it.
By combining player modelling with player-directed content generation, it is a possible
that a synergistic effect could accelerate the search and also produce a more diverse set of
content. When players are exposed to candidate content that they are likely to find interesting,
their discernment in principle can help to explore the subspace of that particular type of
content with more fidelity than would be possible through the overall player population.
Relates directly to challenges: representing style, search space construction.
5 Conclusion
This chapter presents three grand goals for procedural content generation, and presents
several challenges that should be addressed in order to realise these goals, and a sample of
actionable steps that could get you started towards the challenges. Obviously, these are not
the only conceivable actionable steps nor even the only challenges for PCG. We believe PCG
presents a rich and fertile soil for research and experimentation into new techniques, with
obvious benefits both for industry and for the science of game design.
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Chapter 05
... These causally generated assets promise players exploration of vast living environments. They in part respond to calls from gamer and developer communities for multi-level multi-content PCG (Togelius et al. 2013) or perpetual uniqueness (Short and Adams 2019, 15), where wildly different game worlds with all the associated content are generated at the click of a button, with memorable characters guaranteed to appear on demand. There is also community appetite for general content generators (Cook et al. 2019;Togelius et al. 2013), which can be used in multiple contexts, and are easily deployable for a wide range of projects. ...
... They in part respond to calls from gamer and developer communities for multi-level multi-content PCG (Togelius et al. 2013) or perpetual uniqueness (Short and Adams 2019, 15), where wildly different game worlds with all the associated content are generated at the click of a button, with memorable characters guaranteed to appear on demand. There is also community appetite for general content generators (Cook et al. 2019;Togelius et al. 2013), which can be used in multiple contexts, and are easily deployable for a wide range of projects. These help to proliferate PCG techniques and systems to different groups of designer-developer users, both in industry and academia. ...
... We were motivated to appropriate cybernetics, specifically Ashby's Principle of Requisite Variety (1956) and Stafford Beer's Viable System Model (1984), as a means of balancing novelty and coherency in output variety, using alien civilisations and their cultural sign systems for demonstration. Furthermore, designing PCG algorithms with limited variety means that they are often not reusable outside of their original purpose (Togelius et al. 2013). We were motivated to investigate to what extent cybernetics could help us design more generalisable, easy to use PCG. ...
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... There are many different approaches to developing PCG algorithms. These include ad hoc algorithms (used in Rogue), formal languages [34], evolutionary search-based methods [55,58], exhaustive search [51], and more recent machine learning approaches [30,45,52]. Different methods also have different goals, such as providing a consistent quality of levels, or quickly generating playable [23], diverse [29] or configurable levels [10]. ...
... There are many PCG techniques, and some synopses of the field are given in (Smith 2014;Hendrikx et al. 2013). Recent works have considered how to create engaging (Togelius et al. 2013), diverse (Liapis, Yannakakis, and Togelius 2015), and interactive Smith 2014) content. Machine learning techniques can be applied to PCG problems in different ways, as content evaluators or to generate content directly (Summerville et al. 2017;Togelius et al. 2011). ...
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