# Boosting Mixed-Initiative Co-Creativity in Game Design: A Tutorial

Solange Margarido<EMAIL_ADDRESS>0000-0002-8039-7188 ,  Licínio Roque
<EMAIL_ADDRESS>0000-0002-1911-2788 ,  Penousal Machado<EMAIL_ADDRESS>0000-0002-6308-6484 and  Pedro Martins<EMAIL_ADDRESS>0000-0002-3630-7034
University of Coimbra, CISUC, Department of Informatics
EngineeringCoimbraPortugal

###### Abstract.

In recent years, there has been a growing application of mixed-initiative co-
creative approaches in the creation of video games. The rapid advances in the
capabilities of artificial intelligence (AI) systems further propel creative
collaboration between humans and computational agents. In this tutorial, we
present guidelines for researchers and practitioners to develop game design
tools with a high degree of mixed-initiative co-creativity (MI-CCy). We begin
by reviewing a selection of current works that will serve as case studies and
categorize them by the type of game content they address. We introduce the MI-
CCy Quantifier, a framework that can be used by researchers and developers to
assess co-creative tools on their level of MI-CCy through a visual scheme of
quantifiable criteria scales. We demonstrate the usage of the MI-CCy
Quantifier by applying it to the selected works. This analysis enabled us to
discern prevalent patterns within these tools, as well as features that
contribute to a higher level of MI-CCy. We highlight current gaps in MI-CCy
approaches within game design, which we propose as pivotal aspects to tackle
in the development of forthcoming approaches.

Mixed-initiative, human-AI co-creativity, co-creative systems, video games

††copyright: none††ccs: Applied computing Computer games††ccs: Human-centered
computing Collaborative interaction††ccs: General and reference Surveys and
overviews

##  1\. Introduction

Mixed-initiative and co-creativity are two increasingly prominent subjects
within the research areas of human-computer interaction and computational
creativity. As AI systems are becoming increasingly powerful and
sophisticated, we are shifting from the view of AI as a tool towards AI as an
active participant. Thus, there is a growing concern among researchers in
exploring how we can benefit from human-AI partnerships. Particularly, there
has been extensive research into the interaction between humans and
computational systems to foster a collaborative environment where both agents
exhibit initiative and proactivity, hopefully enhancing the creative process
(Yannakakis et al., 2014). As stated by Davis(Davis, 2013), “human-computer
co-creativity describes a situation in which the human and the computer
improvise in real-time to generate a creative product”. Mixed-initiative
systems were defined in 1970 by Carbonell (Carbornell, 1970) as those “in
which both humans and machines can make contributions to a problem solution,
often without being asked explicitly”. More recently, Yannakakis et al.
(Yannakakis et al., 2014) introduced the term mixed-initiative co-creation,
describing it as “the task of creating artifacts via the interaction of a
human initiative and a computational initiative”, where they emphasize the
proactivity of both parties. Although the use of different terms to describe
mixed-initiative may cause some ambiguity, the addition of co-creativity, or
an alternative term such as human-computer co-creativity, can serve as a
highlight of the close collaboration among the agents and the creative nature
of the process and its resulting output. Throughout this paper, we refer to
_mixed-initiative co-creativity_ (MI-CCy) as we are focusing on a strong
creative partnership between humans and computers.

Video games are a domain of particular interest for the application of mixed-
initiative co-creative approaches. As the market grows and the players’
expectations increase, so does the time, money, and effort needed to create
games with large amounts of unique content that meet the expected quality
(Shaker et al., 2016; Baldwin et al., 2017a; Walton et al., 2022). For this
reason, several support tools (e.g. level editors or game engines) have been
developed to streamline game development processes (Yannakakis et al., 2014).
Along with these, there has been major research and development of tools to
automate content generation, known as procedural content generation (PCG)
(Shaker et al., 2016; Baldwin et al., 2017a). While very beneficial, there is
no human-computer co-creation in the use of these tools. When using support
frameworks, the creative process relies merely on human initiative. As for
PCG, the human creative input is very limited, mainly consisting of the choice
of parameters before and/or after the generative process (Yannakakis et al.,
2014). The designer’s lack of control often results in a lower quality of user
experience, with generated content coming off as uninteresting and predictable
(Karavolos et al., 2015; Shaker et al., 2016). Mixed-initiative approaches
applied to game design have the potential to address these issues, as humans
and machines work together, both proactively contributing to the creative
process.

In this tutorial, we provide directions for researchers, developers, and
designers to effectively incorporate a high level of mixed-initiative co-
creativity into their work within the game design domain. Readers will gain an
understanding of the characteristics and shortcomings of existing mixed-
initiative co-creative approaches for various types of game content. They will
also learn about the key elements that foster a balanced partnership and
seamless collaboration between agents and how to identify and analyze these
elements within a mixed-initiative co-creative tool. In Section 2, we
establish a categorization for game content. We follow in Section 3 with an
overview of selected works for each class of game content, summarizing their
features and how they foster co-creativity between human and computational
designers. In Section 4, we introduce a framework for assessing the level of
MI-CCy using rating scales for each criterion, and we analyze the selected
works through the use of this framework. In Section 5, we discuss the
effectiveness and impact of these solutions while identifying some open
issues. Finally, we conclude the survey in the last section.

##  2\. Classes of Game Content

Game design is about deciding everything that a game should be, that is,
everything that the player experiences (Schell, 2008). As this concept is very
broad, we must break it down. Therefore, to allow for categorization of the
works presented in Section 3 by the tackled game design domains, we present
six classes of game content, starting from simple and concrete units to more
complex and conceptual aspects. These classes were defined based on the
classes of procedurally generated game content presented by Hendrikx et al.
(Hendrikx et al., 2013), as it can closely apply to game content in general.
Some changes were made to include a greater amount of content that was not
mentioned by Hendrikx et al., such as objects, characters, physics, mechanics,
and user interface. Furthermore, we strictly focus on in-game content,
excluding derived content such as news and broadcasts of games.

##### Game Bits

Game bits are “elementary units of game content” (Hendrikx et al., 2013), in
which, like Hendrikx et al. (Hendrikx et al., 2013), we include textures,
sound, buildings, and elements of nature such as vegetation, fire, water,
stone, and clouds. We also include 2D image backgrounds, objects of any kind
(2D or 3D representations), characters (only their visual representation, not
their personality and behavior), and user interface elements such as buttons.
These elements can be in their look and shape, or they may still be in a
sketch state.

##### Game Space

The game space represents the environment in which the game unfolds. It can
range from a preliminary outline of the spatial structure, the components it
may contain, and their respective placements, to a high-level representation,
complete with the final look and all the associated game bits in place. When
talking about game space, we mainly refer to maps, which can be indoor or
outdoor and are composed of navigable space, obstacles (e.g., walls, rocks,
and rivers), and interactive elements, such as collectibles. Maps may also
have special features such as teleportation areas. Some common map types
include dungeons, consisting of a group of rooms interconnected by corridors
containing enemies and treasures (Dahlskog et al., 2015), and platform games,
which usually feature suspended platforms with varying shapes and sizes placed
at different heights.

##### Game Behaviors

In this tutorial, we define the game behaviors class as the predefined way
game elements such as objects and characters act and interact with their
surroundings. This includes their relationship with the physics of the game
(e.g., whether they float or react to gravity) and their behavior in different
kinds of interactions such as moving, expanding, exploding, and
disintegrating. It also includes the various mechanics of each game element
and their specific characteristics (e.g., jumping, running, throwing, and
sliding). The characters’ own way of speaking and acting and their personality
traits are also associated with game behaviors.

##### Game Systems

Game systems combine sets of rules and algorithms to provide and manage
complex (and often sustainable) interactions between game elements at a large
scale. Hendrikx et al. identify four main types of game systems: ecosystems,
which dictate the evolution and interplay of plant and animal life; road
networks, which affect traffic, resource exchanges, and access to services in
outdoor locations; urban environments, concerning the efficient organization
of clusters of buildings for the resident population; and entity behavior,
such as complex interactions of non-playable characters with the player and as
a group (Hendrikx et al., 2013).

##### Game Scenarios

Game scenarios outline the sequence of game events and how they unfold
(Hendrikx et al., 2013). In earlier stages of game design, game scenarios can
be portrayed through media like storyboards or diagrams. The story of a game
is a prime example of a game scenario, primarily prominent in narrative-driven
games. Puzzles provide a space of possible solutions that may require the
occurrence of very specific orders of events. Moreover, this class encompasses
the strategic organization of game levels and the careful planning of level
progression in accordance with an underlying mission or set of objectives.

##### Game Design

Finally, we must also refer to the most general and conceptual aspects of a
game such as rules, goals, genre, aesthetic theme, historical context,
depicted reality/universe, and narrative tone. Converting these concepts and
ideas into a game involves exploring the various classes of content mentioned
above. In fact, a game can simply be designed in our head (Schell, 2008), but
it is by going through the myriad steps of game content creation that we can
turn it into a physically playable artifact.

##  3\. Mixed-Initiative in Game Design: Literature Review

This section gathers a selection of current works involving the development
and exploration of mixed-initiative approaches for game design processes. The
selection made was based on the search for the keywords “mixed-initiative”
along with “game design”, “game” or “games” in Google Scholar, focusing on
relevance and topicality. The same search combinations were made by replacing
“mixed-initiative” with “human-computer co-creativity”, “human-computer co-
creation”, “human-AI co-creativity”, “human-AI co-creation” and “co-creative
AI” to ensure that no work would be missed due to the use of different terms
to express the same idea. To keep a focus on video games, the selection of
works we conducted prioritized mixed-initiative approaches exclusively applied
to game design contexts. While we did come across some studies that could
potentially have application in game design but did not primarily target it,
such as mixed-initiative approaches for writing poetry (Oliveira et al., 2017;
Chakrabarty et al., 2022) or creating narratives (Samuel et al., 2016;
Stefnisson and Thue, 2018; Kreminski et al., 2022; Yuan et al., 2022; Lee et
al., 2022), we chose not to include them as our case studies.

We decided to narrow down our selection of works to ten candidates in order to
have a sample size that is sufficiently small to allow for a detailed analysis
yet large enough to be representative of the state of the art. We aimed to
include at least one example for each class of game content. For those classes
that are more prevalent in MI-CCy systems, we included more than one exemplary
work, reflecting the prevailing trends in the field. These classes are game
space, with a particular focus on map design, followed by game scenarios,
which places greater emphasis on narrative design, and then game bits. The
game systems class does not have any work selected to be analyzed with our
framework due to the lack of literature that sufficiently meets our MI-CCy
criteria. In determining which works to include for each game content class,
our primary criterion was to feature recent works, as well as works that held
significant relevance in this research area. We sought to ensure there were
substantial differences among the various candidates to guarantee diversity in
our study. Another pivotal criterion in the selection process was the
availability of the system online, either through a web application or
repository, for testing purposes. The direct interaction with the systems
allowed us to conduct a more in-depth analysis. The works are presented below,
grouped according to the class of game content most prevalently addressed by
each approach.

###  3.1. Game Bits

Tools specifically aimed at creating game bits based on a mixed-initiative co-
creative approach are scarce in the literature. There are several co-creative
systems for art creation; however, that can easily be used to produce game
content. This is the case of systems for co-creative sketching, such as
Creative Sketching Partner (Karimi et al., 2020), where a user starts drawing
and can ask the artificial agent for inspiration at any time. The system
responds with a sketch with a desired amount of visual and conceptual
similarity to the user’s sketch. Then, the user may be inspired to make
changes to their sketch and repeat the process if they wish to. Despite the
diversity of mixed-initiative tools potentially applicable to game design, to
stay on subject, our literature selection only admits works that specifically
target game content creation. An initial endeavor in mixed-initiative targeted
at generating game bits is the work of Liapis et al. (Liapis et al., 2012a, b)
They introduced an approach for generating personalized content based on user
preference regarding visual aesthetics. The framework presents eight
spaceships that were optimized offline, from which the user selects their
preferred one. The system then generates a new set of spaceships, enhancing
the visual features behind the user’s choice via an adaptive aesthetic model.
After a few iterations, the newly evolved content is more likely to be
appealing to the user.

We have chosen more recent literature to include in this class of game
content. We describe two works below, which will be subject to further
analysis using the framework proposed in section 4.

####  3.1.1. AI Spaceship Generator

Gallotta et al. (Gallotta et al., 2023) presented an approach for content
generation aligned with the user’s preferences by incorporating preference
learning into quality-diversity algorithms. To showcase this approach, they
implemented an application to generate 3D spaceships for Space Engineers, a
game that allows players to construct block-based structures. The interface of
the AI Spaceship Generator (Fig. 1) presents a grid with an initial population
of spaceship suggestions. The user can select any of these suggestions and see
its 3D preview along with a list of its in-game properties. If the user
appreciates the selected spaceship, they can press the “Evolve from Random
Spaceship” button and the system will generate more suggestions via a
predictive model of the preferences of the human designer. Otherwise, the user
can press the “Evolve from Random Spaceship” button to be presented with more
random suggestions. As the spaceship population is updated, the user can
select a new spaceship and repeat the process. With each generation based on
user selection, the system continues to learn preferences for the newly
evolved content to stay aligned with the user’s intent. The user can also
change the color of the spaceship and a few generation settings. The spaceship
resulting from the iterative process can be downloaded and imported into Space
Engineers.

Extending procedural content generation systems to mixed-initiative settings
by solely accounting for user selection is a common but limited approach if
the aim is to afford human-computer co-creativity. We discuss this further in
Subsection 4.2.

Figure 1. Interface of the AI Spaceship Generator (Gallotta et al., 2023). Top
left - the spaceship population; top middle - 3D preview of the selected
spaceship; top right - a list of the spaceship properties; bottom left -
controls to generate more spaceships or reset the population; bottom middle -
option to change the spaceship color and option to download its blueprint;
bottom right - application logs. (Available at
https://github.com/arayabrain/space-engineers-ai-spaceship-generator)

####  3.1.2. Sprite Editor for Pixel Art Characters

Coutinho and Chaimowicz (dos Santos Coutinho and Chaimowicz, 2023) introduced
an approach for creating sprites for pixel art characters facing four
directions (front, back, left, and right). To this end, they developed a
sprite editor (Fig. 2) in which the human agent can interact with the
computational agent to generate the various poses. The system contains a main
canvas for the user to view and edit their selected pose. The human agent
initiates the collaborative process by starting to draw the character on the
main canvas. At any time, the designer can drag the pose they drew into a
suggestion slot for a different pose (e.g., dragging the front view into the
right view suggestion slot). A trained generative model takes this image as
input and generates an image for the target pose, displaying it in the
suggestion slot. The human agent can engage in multiple iterative steps,
either directly editing any pose view or asking the computational agent to
generate a new suggestion from any view that is not the same direction as the
target.

Figure 2. Interface of the sprite editor (dos Santos Coutinho and Chaimowicz,
2023). Left - the toolbar; middle - the main canvas displaying the selected
pose view; right - a panel with the four pose views and a panel with the
suggestion slots for each pose view on its left side. (Available at
https://fegemo.github.io/sprite-editor-gm/)

###  3.2. Game Space

Game space is the main field of application of mixed-initiative approaches to
game design. A fair sample of current works is concerned with the level design
domain, in which the game space creation is usually done through the
positioning and rearrangement of tiles or components to form a map in a way
that results in a valid playable level. Some of the tools we analyzed within
this domain include Tanagra (Smith et al., 2010, 2011), Ropossum (Shaker et
al., 2013b, a), the progression design tool for the Refraction game (Butler et
al., 2013), Ludoscope (Karavolos et al., 2015), Evolutionary Dungeon Designer
(Baldwin et al., 2017a, b; Alvarez et al., 2018a, b, 2019), Mixed Initiative
Procedural Dungeon Designer (Walton et al., 2022), Morai Maker (Guzdial et
al., 2019), Lode Encoder (Bhaumik et al., 2021), and Baba is Y’all (Charity et
al., 2020, 2022). The level representation (e.g., dungeons, platforms) for
each of these tools depends on their targeted game genre. We have selected
three works for in-depth analysis, which we present below.

####  3.2.1. Sentient Sketchbook

Liapis et al. (Liapis et al., 2013; Yannakakis et al., 2014) created a tool
for the design of game levels. The Sentient Sketchbook (Fig. 3) enables users
to create and edit levels using map sketches as simplified representations of
levels. As the human designer engages with the tool through sketching, the
system dynamically provides suggestions that are evolved variations of their
current sketch. These suggested alternative map designs are generated using a
genetic search algorithm that aims to optimize the levels’ fitness scores or
produce visually diverse maps. The tools’ interface displays the fitness
scores and other metrics of the current sketch for the user to compare it to
the scores and metrics of each suggestion. The human agent can choose to
replace their current sketch with a suggested alternative at any time. Once
satisfied with the outcome, the designer can request the system to convert the
map sketch into a playable level with enhanced detail.

Figure 3. The Sentient Sketchbook tool (Yannakakis et al., 2014). Left - the
sketch editor; middle - the tile palette, the map display menu, and an
overview of the map’s fitness dimensions and metrics; right - the
automatically generated map suggestions. (Available at
https://www.sentientsketchbook.com/download.php)

####  3.2.2. RL Brush

Delarosa et al. (Delarosa et al., 2021) presented a mixed-initiative tool for
designing tile-based levels, with a version specifically tailored for the
puzzle game Sokoban. RL Brush (Fig. 4) uses a set of AI models to
interactively suggest incremental modifications while the human designer is
editing the level. The system presents four suggestions at a time generated by
reinforcement-learning-based agents: a _narrow_ agent that can change a
specified tile at each step, a _turtle_ agent that can move to a next tile in
the map grid and modify passed tiles, a _wide_ agent that has control to edit
any tile in the map, and a _majority_ agent that suggests tile mutations if
the majority of the other three agents have suggested that same mutation. The
display of new suggestions is triggered by user interactions only. The user
can accept any of these suggestions or override them by manually editing the
grid for the current level. The tool also allows the user to fine-tune the
performance of the AI models by providing two hyperparameters for the user to
control. The _step_ parameter defines the number of interactions for the
manager of the models to recursively call itself so that a higher step value
allows for more than one modification to the map. The _tool radius_ parameter
controls how big is the window of tiles that the artificial agents can view
and modify. By generating modifications one step at a time, this approach
results in a creative process in which participants gradually build the level
tile by tile.

Figure 4. The RL Brush tool (Delarosa et al., 2021). Top - the _step_ and
_tool radius_ parameters; left - the toolbar to edit, erase, delete, play, and
save the current level; middle - the editable grid of the current level and
the four AI suggestions on top; right - the tileset. (Available at
https://rlbrush.app/)

####  3.2.3. miWFC

Langendam and Bidarra (Langendam and Bidarra, 2022) introduced a mixed-
initiative approach to Wave Function Collapse, which can be used to create
various types of maps or other assets such as textures and 2D objects. The
miWFC tool (Fig. 5) incorporates several interactive features that grant
greater control to the human designer over the content generated by the
system. While the system displays the gradual generation of the image through
an animation, the tool enables the user to navigate through the timeline of
the generative process. At any point, the user can add markers to register the
current progress and easily backtrack to any of these markers if desired.
Additionally, they have the option to advance or reverse step by step through
the generation. The human agent can also directly manipulate the tiles of the
image at any stage of the generative process. Several tools are available for
them to add desired tiles or select areas of the map to erase or preserve.
Finally, the tool allows the human agent to control several properties of the
tiles prior to generation, which can significantly influence the output of the
computational agent. These properties include the ability to exclude specific
tiles, set weights to prioritize the appearance of certain tiles over others,
determine whether tiles can be rotated and/or mirrored, and even manipulate
the distribution of tile weights across the output space using heatmaps.

Figure 5. The miWFC tool (Langendam and Bidarra, 2022). Left - a list with
several example input images with the selected one displayed; middle - the
panel for manipulating tile properties; right - the current image accompanied
by a timeline indicating the progress of the generation and a number of
controls for adding markers and navigating through the timeline. The screen
for direct manual editing is accessible from the brush button in the bottom
right corner. (Available at https://github.com/ThijmenL98/miWFC)

###  3.3. Game Behaviors

To the best of our knowledge, there is a dearth of mixed-initiative approaches
specifically targeting game behaviors, such as the mechanics of game elements.
Therefore, we present here a single tool that we have identified as fitting
into this category.

####  3.3.1. Pitako

Machado et al. (Machado et al., 2019a, b) developed a tool for designing tile-
based games that applies the concept of a recommender system to suggest
mechanics and dynamics learned from existing games. The system is built on
Video Game Description Language (VGDL), which considers sprites as objects
with a graphical representation and an associated behavior. The creative
process can begin by adding sprites to the game in development. For instance,
the user can define a sprite behavior as a ShootAvatar and then attach the
image of the hero to it. Having a catalog of a vast number of games, the
system can provide suggestions of sprites to the designer, who is free to
accept them or not (Fig. 6). If the user accepts a recommended sprite, it is
imported to the game sprite set along with any others it may carry (e.g.
picking a sprite that can shoot will also import the sprite for the projectile
behavior). In addition, the system also suggests interactions for pairs of
sprites (e.g. enemy sprite kills avatar sprite), taking its cues from the
existing games in the catalog. If the user selects a recommendation, the
interaction is automatically implemented in the user’s game.

This mixed-initiative approach is primarily targets content creation within
the class of game behaviors. However, it also extends to the game space and
game design classes by enabling the placement of sprites on the level grid and
the definition of game termination rules. Still, it only serves as a support
tool for these two classes as the computational agent lacks initiative.

Figure 6. Sprite Recommendation in the Pitako tool (Machado et al., 2019a).
Top - a menu featuring tabs for the several tasks within the game design
process; left - the game’s sprite set, initially containing only a default
background sprite; center - the sprite property editor, enabling the selection
of an image and the configuration of its behavior; right - a list of the
system-generated suggestions. (Available at https://github.com/tiago-
lam/Recommender-System-for-Game-Design)

###  3.4. Game Systems

The literature review has revealed a gap in mixed-initiative works focused on
the creation of game systems. Most approaches are merely generative, lacking
an iterative process between humans and computers. Such can be attributed to
the nature of this type of game content, which often aims to simulate real-
life interactions (e.g., the behavior of an army in battle) and therefore
requires little creativity. Thus, the mere generation of game systems may be
satisfactory enough for designers. It is also a demanding task that involves
programming complex interactions among multiple entities. Furthermore, it
requires the in-game simulation of these interactions for assessment and
refinement, which can be highly time-consuming and significantly prolong each
iteration. We were only able to find one work in the scope of game systems
that we could classify as a mixed-initiative approach.

####  3.4.1. EvolvingBehavior

Partlan et al. (Partlan et al., 2022) introduced a tool for crafting non-
player character (NPC) behavior by evolving behavior trees in Unreal Engine 4.
The process begins with the human designer providing an initial draft of a
tree and other materials, such as a library of pre-defined nodes and templates
for generating additional nodes. The system can then modify and generate
behavior trees, even if the initial tree is empty or incomplete. In order for
the evolution of behavior trees to meet the goals of the human designer, they
must define a fitness function by setting fitness measurement keys. The user
also needs to define several parameters of the evolutionary process. Given
that the evolved trees can be manually edited, in practice, the human designer
can modify them and use them as input for a new round of evolution, engaging
in an iterative process. However, this work was not selected for further
analysis in section 4. This is because it is currently in a state where user
interaction with the tool is too complex for a regular designer without expert
knowledge of the system and coding skills to easily work with it.
Particularly, it is challenging to define the fitness function and adjust all
the parameters without prior knowledge. Additionally, the generation and
subsequent simulation of behavior trees are time-consuming. The delay would
likely discourage designers from participating in a new evolutionary process.

###  3.5. Game Scenarios

Narrative is a type of content within the game scenarios class that is highly
popular in co-creative tools outside the domain of video games. While not
their specific focus, most of these tools can be easily applied to game
design, particularly for story-driven games. Large language models like GPT-3
(Brown et al., 2020) can be used for the purpose of generating stories. An
example of this is Wordcraft (Yuan et al., 2022), a text editor for co-
creative story writing with the assistance of a large language model. However,
co-creative storytelling does not necessarily entail both agents contributing
to the narrative in prose form. In Writing Buddy (Samuel et al., 2016), the
computational agent offers suggestions of character actions that are
consistent with the narrative. This tool offers a playful setting for co-
creative writing by establishing narrative goals for the user to complete.
Similarly, Loose Ends (Kreminski et al., 2022) provides the user with
narrative goals that involve a structured sequence of plot events and can
propose new goals consistent with the evolving story throughout the creative
process.

In some cases, narrative design is not solely conducted during game
development but also involves in-game content creation. This occurs when
narrative creation is either the very concept of the game or is integrated
into its dynamics, such as in dialogues between the player and non-player
characters. Below, we present two distinct MI-CCy approaches for game
scenarios: one that applies the creation of narrative structures to a specific
game genre and another in which storytelling is an integral part of the game
itself.

####  3.5.1. Story Designer

The Evolutionary Dungeon Designer (EDD) is a mixed-initiative tool for
designing 2D dungeons and the rooms that compose them. EDD is part of an
ongoing research project and has been progressively expanded over several
works (Baldwin et al., 2017a, b; Alvarez et al., 2018a, b, 2019, 2021). As a
next step, Alvarez et al. (Alvarez et al., 2022) built an approach to design
narrative structures integrated in EDD. Story Designer (Fig. 7) uses tropes in
the form of nodes that can be interconnected in a graph to construct a
complete narrative structure. The creative process begins with the system
providing a basic sample graph HERO → CONFLICT → ENEMY. Users can manually
edit the narrative graph by adding and connecting nodes representing
characters, their roles, goals, and events. This triggers an evolutionary
algorithm that generates alternative suggestions of narrative structure in
real time that the user can inspect and choose to replace the current
narrative design. The algorithm is an adaptation of MAP-Elites, which
evaluates the set of solutions according to metrics such as interestingness
and coherence. The system displays the suggestions in a matrix arranged by
their values across specified dimensions of interest. Solutions with optimal
fitness are shown in dark green, while dark red cells mean the best solution
for those values is still not feasible. The human designer can visualize two
dimensions at a time a switch between them as needed. As Story Designer is
linked to the level design tool of EDD, the suggested narrative graphs are
constrained by the existing content in the level’s map.

Figure 7. The Story Designer tool (Alvarez et al., 2022). Center - The editor
for the main narrative graph; right - the suggestion grid, the list of
dimensions that the user can choose for the grid, the suggestion they are
hovering, and the selected suggestion; bottom - display of fitness metrics of
the main graph and of the suggestions. (Available at https://github.com/mau-
games/eddy)

####  3.5.2. 1001 Nights

Sun et al. (Sun et al., 2022) developed 1001 Nights, a co-creative
storytelling game that transforms story elements into game mechanics. The game
features two characters: Shahrzad, controlled by the player, and the King,
controlled by an AI model. The player needs to tell a story by typing it into
a chat-like interface (Fig. 8), and the King responds by continuing the tale.
Consequently, the narrative unfolds through alternating turns. If the King
mentions items such as “sword”, “dagger”, and “shield” in the story, the
player can convert these words into weapons within the game to use in a battle
against the King. Thus, the goal is for the player to narrate a story that
prompts the system to include these words. This goal serves to constrain the
content generated by both the human and the system, ensuring the story remains
on topic. To encourage the player to continue developing the narrative, the
game requires them to possess at least two attack weapons to be able to defeat
the King. The words the game allows to convert into equipment are consistent
with the ancient context of the classic One Thousand and One Nights folktales.

Figure 8. The 1001 Nights game (Sun et al., 2022). Left - a dialogue interface
containing text blocks from the story told by the player and the King, along
with a text box at the bottom for the player to write; right - a screen
featuring the game characters, in which the player can collect the generated
items and add them to the inventory at the bottom; bottom right corner: the
button to initiate the battle. (Available at https://cheesetalk.itch.io/one-
thousand-and-one-night)

###  3.6. Game Design

There are few MI-CCy approaches that address high-level concepts, such as the
rules and objectives of a game. Most existing tools are already tailored for a
specific game or game genre. So far, when the creative task of involves
ideating a novel game, it is still uncommon to grant decision-making authority
to the computational agent, thus leaving it with a passive role in the
process. Nevertheless, there are some works that deviate from this pattern.
Among the works we have reviewed, CADI and Gamika fall into this category but
were not selected. CADI (Mobramaein et al., 2018) is a tool that employs a
conversational interface for agents to design variations of the game Pong.
Gamika (Nelson et al., 2017) is a system for creating 2D physics-based games
for mobile devices, which parametrizes the design space into 284 features that
the user can manipulate.

####  3.6.1. Germinate

Kreminski et al. (Kreminski et al., 2020) introduced a mixed-initiative casual
creator for designing rhetorical games. This framework builds upon Gemini, an
abstract game generator, but has a more user-friendly interface tailored for
casual users. Through the browser-based interface, human agents can specify
certain properties they wish to see in the generated games. These properties
are defined using four types of cards: entity cards, which represent game
objects with a graphical representation (emoji) that react to interactions
with the player or other entities; resource cards, which describe quantitative
values linked to the objectives of the game; relationship cards, which
establish connections between entities and/or resources; and trigger cards,
which describe the outcomes of specific trigger events. Gemini then generates
a batch of games based on these specified properties, which are displayed in
the interface of the tool. Each game includes a set of rules, and respective
behaviors and interactions that align with the user’s expressed intentions.

For instance, a user interested in creating a stress-themed game might define
entities such as “Friend” and “Work”, a resource called “Stress”, and the
relationships and triggers they want to showcase. This example is illustrated
in Fig. 9. The user can explore the several generated games to assess if there
are any interesting results and then make adjustments to the properties to
better reflect their intent, allowing for iterative refinement of the game
design.

This approach is focused on starting from high-level rhetorical goals and then
working on details to meet those goals. To articulate the conceptual essence
of the game, Germinate also relies on the creation of a game system and game
behaviors.

Figure 9. The Germinate tool (Kreminski et al., 2020). Left - properties
specified by the user; center - a playable version of one of the generated
games (a single game is shown at a time); right - the properties of the
generated game. (Available at
https://github.com/ExpressiveIntelligence/Germinate)

###  3.7. Overview

Each work presented in this section falls into one of the six classes of game
content we previously defined. However, these tools may tackle more than one
class. Table 1 provides a list of the selected works, with all covered classes
of game content explicitly marked.

Table 1. Categorization of Works by the Tackled Classes of Game Content Work |  | Game
---
Bits
| Game
---
Space
| Game
---
Behaviors
| Game
---
Systems
| Game
---
Scenarios
| Game
---
Designs
| AI Spaceship Generator
---
Gallotta et al. (2023)
x |  |  |  |  |
| Sprite Editor for Pixel Art Characters
---
. Coutinho and Chaimowicz (2023)
x |  |  |  |  |
| Sentient Sketchbook
---
Liapis et al. (2013)
| x |  |  |  |
| RL Brush
---
Delarosa et al. (2021)
| x |  |  |  |
| miWFC
---
Langendam and Bidarra (2022)
x | x |  |  |  |
| Pitako
---
Machado et al. (2019)
| x | x |  |  | x
| EvolvingBehavior
---
Partlan et al. (2022)
|  | x | x |  |
| Story Designer
---
Alvarez et al. (2022)
|  |  |  | x |
| 1001 Nights
---
Sun et al. (2022)
|  |  |  | x |
| Germinate
---
Kreminski et al. (2020)
|  | x | x |  | x

##  4\. Mixed-initiative Classification

Getting started with mixed-initiative approaches often requires acquaintance
with the various existing systems and identifying what distinguishes them,
what techniques are used, what is novel about them, how is mixed-initiative
present, what role the human and the computer play, among other questions.
Surveys and overviews in the literature serve as valuable resources for
providing a broad introduction to the subject. In particular, Lai et al.(Lai
et al., 2022) provide an overview of the techniques commonly used in mixed-
initiative systems for game content creation and its inherent challenges.
While researchers and practitioners undoubtedly benefit from this knowledge,
we emphasize that individuals implementing a MI-CCy system should, foremost,
reflect on what kind of human-computer interaction they want to convey and
then choose the technique that will better deliver that intent. To do so, it
is also notably useful to have tools that allow us to carry out our own
analysis when designing a mixed-initiative system so we can reflect on its
novelty and contributions to human-computer co-creativity.

Deterding et al. (Deterding et al., 2017) emphasize how the human and the
computer iteratively respond to each other through a variety of possible
actions. Following this idea, Spoto and Oleynik (Spoto and Oleynik, 2017)
introduced a framework that allows the comparison of mixed-initiative systems
regarding the flow of the creative process. The flow is visualized through a
graph that considers seven subprocesses of the creative process, namely
ideate, constrain, produce, suggest, select, assess, and adapt, which can be
executed by both the human and the computer. The graph illustrates the
sequence of actions through directional links, usually outlining internal
interactive loops. Muller et al. (Muller et al., 2020) expand on this
framework, addressing mixed-initiative scenarios with generative AI systems.
Grabe et al. (Grabe et al., 2022) also adapt the framework to co-creativity
with Generative Adversarial Networks (GANs), identifying four primary patterns
of interaction between humans and GANs. Focused on interaction design in co-
creative systems, Rezwana and Maher (Rezwana and Maher, 2021, 2023) presented
COFI, a framework that describes several interaction components to represent
the space of possibilities for interaction between humans and AI and with the
shared creative content. We consider these frameworks to be valuable, making
it possible to distinguish between approaches in terms of how the creative
process admits initiative from both agents involved and how they interact with
each other. However, they make little to no mention of the degree of mixed-
initiative, the real impact each agent has on the creative process and how
much their collaboration is facilitated.

###  4.1. MI-CCy Quantifier: A Framework for Mixed-initiative Co-creativity
Quantification

Liapis et al. (Liapis et al., 2016) consider it ingenuous to expect human and
computational agents to be equal partners in the creative process. We admit
that participants involved in co-creation can never be exactly equal
throughout the various stages of the process, which is also true for human-
human co-creativity, as each person has their own personality, knowledge,
capabilities, and past experience. Nonetheless, we argue that such should be a
goal for future mixed-initiative tools. Liapis et al. (Liapis et al., 2016)
reiterate that current approaches favor human control, with the user having
the final say on the decisions taken. We hope that new approaches can
challenge this view. It is our belief that working as colleagues in a process
in which collaboration is continuous and facilitated allows the human and the
machine to learn from each other productively and can maximize the
capabilities of both. With this perspective in mind, we present the MI-CCy
Quantifier, a framework for comparing systems with respect to the degree of
mixed-initiative co-creativity. The classification is made independently of
the process flow. In this way, the analyzed parameters are generalized and
applicable to any system, even if the processes are very different in their
form. This model allows for a more direct visualization of which agent has a
greater impact and how seamless their collaboration is. Figure 10 shows the
framework representing a scenario with a very high degree of mixed-initiative
co-creativity.

Figure 10. Representation of a very high degree of mixed-initiative co-
creativity through the use of MI-CCy Quantifier.

We introduce four parameters that allow us to classify the influence that the
human and the computer have on the creative process: _Initial Setting_ ,
_Initiative_ , _Evaluation_ , and _Final Decision_. Each of these parameters
is assessed along a spectrum, with the human at one end and the computer at
the other. We consider that the more equal (towards the middle of the
spectrum), the higher the degree of mixed-initiative. The framework has three
more parameters that classify the approach on how much it promotes seamless
collaboration. They are _Task Assignment_ , _Intervention Pace_ , and
_Explainability_. Each of these parameters comprises its own scale, which is
displayed vertically. We consider that the higher up the scale, the more
seamless the collaboration between the human and the machine will be and,
consequently, the greater the degree of co-creativity.

It should be noted, however, that just because one system has a high degree of
mixed-initiative co-creativity over another does not mean that the approach is
better than the other. It only tells us that the human agent and the
computational agent are more equal partners and are equipped for a high level
of collaboration. Such may or may not be desirable, depending on the intended
applicability of the system. If the user already has an established idea and
only wants help to execute it, they do not expect to have a system that wants
to have a strong initiative in the conceptual phase of the process.
Nevertheless, in this tutorial, we encourage researchers interested in
developing new mixed-initiative co-creative systems to seek to implement an
approach with a higher degree of MI-CCy since there is still little
exploration in the current state of the art of the potential of the computer
as a “true” colleague in the creative process.

####  4.1.1. Initial Setting

The Initial Setting criterion refers to setting the starting point for the
creative process. What will you work on? What problem do you want to solve?
What is the initial concept or rule set? Most of the time, this is defined by
the human creator when working with systems focused on supporting the user. In
these cases, the user already has an established base concept and needs help
to better develop it and turn it into an artifact (dos Santos Coutinho and
Chaimowicz, 2023; Kreminski et al., 2020). In other cases, as in some
approaches that use evolutionary algorithms, the creative process begins with
an initial population generated by the system (Gallotta et al., 2023; Liapis
et al., 2012a, b). The user can only adapt the content from there, which, of
course, substantially limits the creativity of the human.

Ideally, in a collaboration, the initial setting should have input from all
participants in the creative process. We expect that, in a mixed-initiative
approach, the value of this parameter will be somewhere along the spectrum,
not so much at one end (human only) nor so much at the other (computer only).
Imagine a group project in a game design course in which students are asked to
develop a game by the end of the semester. In the first class, all the group
members get together and try to contribute ideas to define the concept of the
game. It may happen that the group discards most ideas, and just the idea of
one participant is used. Nonetheless, all of them influenced the generation of
the initial concept, even if only in the role of accepting or discarding
ideas, in addition to stimulating the creativity of the group with their
suggestions. Even so, it is certain that one of the participants in the co-
creative process will have to give the starting point to initiate the
conversation. A system designed to be a colleague should leave open the
possibility of who will make the first contribution(s). As an example, in
Germinate (Kreminski et al., 2020), the human is the one who defines the first
rule set. We envision that the possibility of asking the computational agent
to do the initial setting could be implemented with little effort.

####  4.1.2. Initiative

The Initiative criterion refers to having strong initiative throughout the
creative process. As the literature on human creativity and computational
creativity shows, there are several ways for the human and the computer to
actively participate in the process. This is evidenced by the frameworks of
Spoto and Oleynik (Spoto and Oleynik, 2017), Muller et al. (Muller et al.,
2020) and Grabe et al. (Grabe et al., 2022), which consider several possible
actions for both agents, as mentioned earlier. With this parameter, we intend
to analyze who has more initiative between the human and the computer. With so
many ways to manifest initiative, it can be difficult to pinpoint where the
system falls along this spectrum. We highlight four elements to consider when
reflecting on the initiative of each co-creator: quantity of interventions,
proactivity, input quality, and variety in the type of actions they can
perform. Who is more involved? Can they contribute voluntarily and
autonomously? Can their input be considered creative? In what ways can they
intervene? We should reflect on these elements together. We might have, for
instance, a computational agent that continuously makes suggestions, but these
can be considered too random, unoriginal, and not adaptable enough to the
partner’s feedback. Furthermore, the system may be limited to making
suggestions without a chance to question high-level concepts or make decisions
about the artifact being produced. In other cases, the interventions of an
agent may be more about assessing what the colleague is doing than
contributing creative input.

We emphasize that we do not intend for this framework to be used to perform
rigorous classificat ion but rather to serve as a tool for reflection on the
mixed-initiative approach one seeks to develop. In human-computer co-
creativity, it is ideal that both the human and the computer can actively and
regularly intervene during the creative process and can do so in different
ways that add value to the generation of the artifact(s). We must then
contemplate whether both agents have the same opportunities for action.

####  4.1.3. Evaluation

The Evaluation criterion refers to assessing the suggestions and artifact(s)
being created. Evaluation can be considered as a subclass of the initiative
parameter, but we decided to place it as an independent parameter, as it says
a lot about who has more control in the process. Who judges if something is
good or not? Who evaluates suggestions and has the power to accept or reject
them? One can contribute ideas in the creative process, but do they have space
to express their opinion on the ideas of others? If one does not like the
output generated in the process, can they criticize it?

When we talk about evaluation, it is not limited to stating whether the
artifact is viable and a possible solution to the problem (e.g., assessing if
a level is playable and balanced). While this objective assessment is pivotal,
it is insufficient in a creative process. It is also necessary to assess
whether the artifact is creative, which may involve considerations of how
novel, interesting, surprising, and valuable it is (Canaan et al., 2018). This
is a more subjective assessment that can potentially lead to disagreements
between the participants involved. What happens in the vast majority of
current works is that, in the event of disagreements, the human agent is the
one who has the power to decide on the course to be taken. In a mixed-
initiative co-creative scenario, we expect both the human and the computer to
evaluate the generated content, express their likes or dislikes, and try to
reach a solution that satisfies both parties.

####  4.1.4. Final Decision

The Final Decision criterion refers to deciding when to end the creative
process. Typically, the process ends when one or both agents consider that the
artifact is finished or the creative problem has been solved. In the current
state of the art, it is very unlike that the computational agent has any say
on this. The human agent is the only one reflecting if a final solution has
been reached and deciding when to conclude the process. The computer can be
included in this decision by expressing how it feels about the state of the
creative process: “I’m happy with this result. Are you?”. Alternatively, it
might disagree with the intention of its co-creator to end the process: “I
don’t feel like we have yet reached an ideal solution to our problem.”.
Through such dialogue, the process will hopefully end when both partners agree
that a final result has been achieved.

This is the last aspect we highlight for researchers to analyze their and
other mixed-initiative systems regarding the role that the human and the
computer play and the impact they have throughout the collaboration.
Reflecting on these four parameters, we get a clearer view of how equal the
participants are in the various stages of the process.

####  4.1.5. Task Assignment

The Task Assignment criterion ranges from working on separate and distinct
tasks to working on the same task. This parameter directly correlates to the
XAID (eXplainable AI for Designers) spectrum of domain overlap described by
Zhu et al. (Zhu et al., 2018), which distinguishes off-task co-creativity from
on-task co-creativity. In on-task co-creative scenarios, agents apply the same
tools to the same task. Rezwana and Maher (Rezwana and Maher, 2021, 2023)
include task distribution as an interaction component in their model,
comprising same task and task divided. The Task Assignment criterion also
relates to Kantosalo and Toivonen’s definition of alternating co-creativity in
contrast to task-divided co-creativity (Kantosalo and Toivonen, 2016). They
emphasize the partners’ concern to satisfy the requirements of both parties in
alternating settings. A mixed-initiative tool should be ranked further down
the scale of Task Assignment if the human and the computer are working on
different tasks so that contributions resulting from one task only slightly
affect the other. Higher up the scale must be systems where agents are working
on the same task, and the input of one agent directly affects the subsequent
intervention of its co-creator. Furthermore, there is another factor to
consider besides working on the same task. It is also necessary to reflect on
how the participants perform the tasks. They may be working on the same task,
but do they perform the same type of actions? Can they make use of the same
tools? An illustrative case is the difference between interventions done by
designing (where manipulation is more direct) or by setting rules and metrics.
The human and computer can both be working on a level design task in terms of
layout. However, if one can arrange the level tiles while the other can only
handle parameters, they are not working on the same task to the fullest
extent.

Admittedly, it may or may not be advantageous to have participants in the
creative process working on the same task. In human-human co-creativity, it is
also common to have a clear task division, as one person may have more skills
for certain aspects of game design and the partner for others. One may be
better at drawing and therefore be in charge of game art, while another is
concerned with building the characters and storyline of the game. However, if
we intend to have a collaborative process in which participants foster the
creativity of each other and seek to achieve a result that pleases both, then
working on the same task as equals is ideal. It is usual to assume that the
computer is incapable of being as creative as the human and to limit the type
of tasks it can do because of that. We therefore hope that future approaches
may counter this preconception.

####  4.1.6. Intervention Pace

The Intervention Pace criterion ranges from long design shifts to immediate
feedback. Whether they are working on the same task or not, how long do co-
creators wait to give and receive feedback? The exchange of ideas is promoted
if the stakeholders can respond almost instantly to additions and changes made
to the creative artifact. The turns taken by the participants should be short
and adapted to the interventions of each other so that both can express their
opinions and preferences throughout the process. In fact, the agents involved
may not even have precisely defined shifts, being able to interrupt the
partner who is designing if they deem it appropriate. This, of course, implies
questions of common sense and mutual respect so that every co-creator has the
opportunity to speak without anyone imposing too much on others with their
interventions. Rezwana and Maher’s (Rezwana and Maher, 2021, 2023) interaction
components of Participation Style and Timing of Initiative relate to the
Intervention Pace criterion. Participation Style distinguishes turn-taking
from simultaneous contribution of both agents. Timing of Initiative
distinguishes planned interventions from spontaneous ones. We consider there
is a causal relationship between these two components, as simultaneous
collaboration favors spontaneity while turn-taking limits it.

In addition to the timing between interventions, we need to reflect on the
size of the content leap they represent. The system may respond immediately to
user feedback, but it can be too big of a jump between user-generated and
system-generated content. In such a case, the response may skip several design
steps ahead without allowing the colleague to intervene halfway through, which
limits the discussion and gradual evolution of the artifact throughout the
process. The beginning of the creative process should be more focused on
defining initial concepts. As such, if a designer is soon confronted with a
finished artifact, they feel less room for exploring and discussing ideas, as
the creative possibilities have already been narrowed.

In conclusion, a mixed-initiative system with a slow intervention pace is
characterized by long design shifts and large content leaps. At the top of the
scale are systems with a fast intervention pace, where feedback is immediate
and there are no extreme leaps in the content created. Although this parameter
should be considered independently of Task Assignment, working on the same
task is more likely to allow for a faster intervention pace.

####  4.1.7. Explainability

The Explainability criterion ranges from a low level of explainability to a
high level of explainability. By explainability, we mean the ability of co-
creators to explain their intentions, creative input, and process. Explanatory
capabilities are critical for streamlining the collaborative process between
humans and computers. As we know, communication is an intrinsic and essential
element in human-human collaboration. Despite that, it is often overlooked in
human-computer co-creativity. Several authors have identified the need to
capacitate computational creativity systems with explainability, identifying
requirements and strategies to do so. Zhu et al. (Zhu et al., 2018) propose a
subfield of Explainable Artificial Intelligence (XAI) focused on the specific
needs of game designers when working with co-creative systems. Another
subfield of XAI for contexts of computational creativity is proposed by Llano
et al. (Llano et al., 2020) They introduce Explainable Computational
Creativity (XCC), specifically concerned with enabling two-way communication
between creative systems and human users. They identify four essential design
principles for co-creative systems to achieve this: mental models, long-term
memory, argumentation, and exposing the creative process. Margarido et al.
(Margarido et al., 2022) expand on how these principles can be applied to
mixed-initiative co-creativity in game design, suggesting several approaches
adapted to distinct use cases. Closely related to explainability, Lin et al.
(Lin et al., 2022) identify three non-exhaustive dimensions of communication
between humans and computational agents: whether communication is human-
initiated or agent-initiated, whether it concerns previous contributions
(reflection) or future ones (elaboration), and whether it focuses on a general
aspect of the creative task (global scope) or an individual one (local scope).

In order to think about the degree of explainability of our mixed-initiative
system, there are several questions we can ask. Are the agents involved in the
process able to explain their ideas? Do they have any way of justifying their
contributions? If so, in what way(s)? What strategies do they use? What are
the means of communication? Is the creative partner able to understand? Can
the partner also explain themself and be understood? Is it possible to solve
through dialogue the conflicts that may arise? Do both agents have room to
further defend ideas that have been rejected? Can they recall past
interactions that are relevant to the present discussion? As we answer these
questions, it becomes increasingly clear whether the mixed-initiative tool has
sufficient explainability. A system with low explainability, and therefore on
the bottom of the spectrum, does not have the means for co-creative agents to
explain and understand the interventions of each other. A system with high
explainability allows for a dialogue very close to what happens in human-human
co-creativity. Incidentally, it may even be an improved version of this
communication, thanks to audiovisual, memory, and processing capabilities that
are unique to computational agents.

It is still challenging to achieve a high degree of explainability today.
However, we incentivize researchers to make this attempt, as it will lead to a
new paradigm for human-computer collaboration. We recommend relying on the
literature to find directions on how to do so.

###  4.2. Literature Analysis with MI-CCy Quantifier

Using the MI-CCy Quantifier, we analyze below each of the works we selected in
Section 3, describing and justifying the classification we gave to each of the
framework’s parameters.

####  4.2.1. AI Spaceship Generator

Initial setting: In this work, the initial setting is exclusively defined by
the computational agent. The creative process begins with the system
generating an initial population of spaceships, and the user forcibly works
from any of these suggestions or requests the system to generate a new
population. The conceptual space predefined by the system is too constrained
to allow extensive exploration and cannot be transformed.

Initiative: The computational agent can only intervene by generating several
spaceship suggestions, while the user can only select one option and request
new generation rounds. On the one hand, the user is the one who decides
whether there will be a new round of spaceship generation and from what point
(whether from the selected spaceship, a random spaceship, or resetting the
population). On the other hand, the user has no way of directly modifying the
artifacts or clearly indicating their preferred characteristics, which
restricts their creativity. The computational agent only generates more
spaceships at the behest of its partner but can create their entire structure,
exerting significantly greater control over the visual appearance of the
artifacts. Thus, we consider the system to have more initiative.

Evaluation: The human agent evaluates the artifacts by selecting the one they
prefer to use as a basis for a new iteration. However, the system can only
predict the reasons that lead the human agent to prefer a particular spaceship
over others. That is, the evaluation criteria of the human agent are assumed.
In theory, the system favors user preferences and adjusts the fitness function
accordingly, but this is not guaranteed in practice.

Final decision: The system provides possible solutions, but it is up to the
user to decide when they are satisfied with a spaceship, ready to finish the
process and import the winning spaceship into the game.

Task assignment: Even though both agents are working on creating spaceship
designs, they do not work in the same way. The computational agent intervenes
by generating the entire structure of the spaceships, while the human agent
can only contribute to the creative process by selecting a spaceship they
prefer among those presented to them. This selection will never have a
disruptive impact on the subsequent intervention of the computer.

Intervention pace: The agents have clearly defined turns. Even if the user is
quick to select one (or none) spaceship to evolve from in the next iteration,
there is a long waiting period where the user cannot do anything nor receive
feedback until the system completes the new generation. Furthermore, the
system responds with new complete spaceship designs, as opposed to short
modifications to the selected spaceship or more open-ended spaceship
prototypes.

Explainability: There is no explainability. Neither the human nor the computer
has the means to express their intentions and justify their contributions.

####  4.2.2. Sprite Editor for Pixel Art Characters

Initial setting: The human agent is the one who initiates the co-creative
process, setting the starting point to generate other poses for the character.
The computer cannot generate meaningful content without sufficiently
comprehensive input from the human partner. Furthermore, if the initial design
of the human agent is scarcely recognizable to the trained model, the quality
of the contributions of the computational agent is compromised.

Initiative: The human agent has the stronger initiative. They can directly
edit any pose view, including those in the suggestion slots. By being able to
draw and promptly modify any artifact, the human agent is granted more
creative freedom. On the other hand, the computer can only intervene upon user
request. As mentioned by the authors, it lacks proactivity to make independent
contributions. The computational agent provides suggestions but cannot
manipulate the artifacts directly. Moreover, it always tries to match the
design provided by the human partner, avoiding the suggestion of images that
are conceptually different from the source. Thus, despite generating new pose
views, the input from the computational agent lacks creativity.

Evaluation: The system provides suggestions that align with the input from the
user without having an opinion about what is being drawn. The evaluation
process is entirely carried out by the human agent, who determines whether to
accept or ignore suggestions and modify the content if they deem it necessary
for improvement.

Final decision: The human designer is the one who decides if and when they are
satisfied with the character sprites.

Task assignment: Both agents are engaged in the creation of sprites for a
pixel art character. The human agent has the ability to draw the character
from scratch, directly manipulate any pose view, and select each image that
will serve as input for a new suggestion. On the other hand, the computational
agent can generate sprites for the character but can only present them as
suggestions. Nevertheless, we can assert that both agents are able to generate
content within the same domain.

Intervention pace: The participants involved in the co-creative process can
engage in a fast-paced interaction loop. Despite having well-defined turns for
each participant (with the system only intervening upon user request), the
response is immediate. In most cases, the suggestions from the system do not
represent an extensive leap in content and are closely aligned with the user-
generated content. Since the human agent has more control in the process, they
can generate from none to a substantial amount of new content between the
interventions of the computational agent. Although the turns are short, the
system cannot intervene spontaneously.

Explainability: There is no explainability.

####  4.2.3. Sentient Sketchbook

Initial setting: The conceptual space is already predefined by the system. The
tool is designed to generate maps for strategy games, meaning that the
available tiles for constructing the map and the fitness dimensions used are
already established for this purpose. Consequently, the human agent is
constrained to work within a specific game style and towards a predetermined
objective. As for initiating the co-creative process, the computational agent
immediately starts offering suggestions, even if the human agent has not yet
begun editing the map. On the other hand, the human designer can start drawing
right away, triggering an instant change in the system’s suggestions and
thereby establishing the starting point for shaping the map. Taking all these
factors into account, we consider that the initial setting criterion leans
slightly towards the computer end of the spectrum due to the conceptual space
being fully predefined.

Initiative: The human agent contributes by directly editing the map sketch and
selecting suggestions. The computational agent intervenes by consistently
suggesting alternative map designs but cannot directly edit the main sketch.
Both agents are highly proactive and engage multiple times throughout the
creative process. The human partner can continuously add tiles and analyze
suggestions, while the computer willingly responds with new creative input
after each contribution made by its counterpart. Thus, both agents are deeply
involved in the process and attentive to each other’s feedback. In this
criterion, the user slightly stands out due to the ability to directly
manipulate the map sketch.

Evaluation: Both evaluate the content being created. The human agent self-
assesses the content they are creating and identifies areas for improvement
over time. Simultaneously, they evaluate the map suggestions of the
computational agent, considering whether any are superior to the current
sketch and possibly choosing to replace it or draw inspiration from certain
features they appreciate in the suggestions. On the other hand, the computer
tests the playability of the main map sketch and evaluates it based on
gameplay properties related to game pace and player balance. It does the same
to generate map designs and choose the best ones to present to the user.
Additionally, half of the map designs generated by the system are evaluated in
terms of novelty, and the system selects the most distinct maps to include in
the suggestions. There is, however, a significant difference in the evaluation
process carried out by the two agents: the system can only disclose its
opinion to the human partner regarding gameplay characteristics. It cannot
express whether it finds a particular map creative or not. Furthermore,
regardless of what the computational agent informs the user about its
evaluation of each of the map properties, only the human designer has the
authority to accept or reject suggestions. Therefore, the human has more
evaluation power than the computer.

Final decision: The human agent determines when they believe the map sketch is
finalized and ready to be exported. Subsequently, the computational agent
generates a detailed view of the map. The human partner cannot directly edit
the detailed version of the map but can choose their preferred option from the
viewing modes provided by the computer or request the generation of a new
view. Thus, the human has the final say over the structure of the level but
has less control over its final look.

Task assignment: The agents are collaborating on the shared task of creating
map designs for a strategy game. The input from the human agent directly
affects each subsequent intervention of the computational agent, whereas the
contributions of the computational agent may or may not impact the next
intervention by the human agent. Although the computer cannot edit the main
sketch and select suggestions, both agents can willingly construct map
designs.

Intervention pace: The co-creators engage in a fast intervention pace, with
short turns involving the addition of small portions of content over time. In
this case, the human agent edits a single tile (or a small area) at a time,
and the computational agent intervenes with each change made. The content leap
between the main map sketch and the computer’s suggestions is also not too
substantial. There is only a larger gap observed at the beginning of the co-
creative process as the system generates suggestions with sufficient content
to be playable. In essence, the agents provide immediate and spontaneous
feedback.

Explainability: The explainability is severely limited. The computational
agent makes a feeble attempt to justify why its suggestions may be preferable
by displaying their respective fitness scores compared to the main map sketch.
Thus, in the case of suggestions aimed at optimizing fitness scores, the human
agent has a means to understand some of the improvements the suggestions offer
to gameplay. However, these properties solely describe functional
characteristics and provide no insight into the creativity of the map design.
Conversely, the human designer lacks any means to explain their intentions and
preferences.

####  4.2.4. RL Brush

Initial setting: The conceptual space is predefined by the system. Although
the RL Brush tool can be applied in theory to any type of tile-based game,
this version of the tool was specifically built to design levels for the game
Sokoban. As a result, only the tiles necessary for this game are made
available. The human agent is restricted to working with these tiles and
cannot add new ones or change the conceptual space. The system also takes the
first step in initiating the creative process. It invariably presents the user
with an empty map containing a player tile at the center, along with four
initial suggestions. Even though the computer starts the co-creative process
by only inserting a tile (rather than presenting a complete level), it can
still slightly influence the direction in which the level will develop.
However, since the human agent can immediately start editing the level, they
can easily change this initial configuration, triggering new suggestions from
the computational agent and taking control of the process.

Initiative: Similar to the Sentient Sketchbook, the human agent directly
impacts the creative artifact throughout the process by being able to manually
edit the level grid or choose a suggestion to replace it. The computational
agent provides suggestions whenever the human partner makes a modification,
but cannot directly edit the current level. This approach has the additional
characteristic that the user can control the performance of the system,
granting them the power to limit the quality and scope of its suggestions.
Consequently, both agents proactively contribute creative input, but the human
designer retains more control over the process.

Evaluation: The evaluation is mostly done by the human agent. They not only
self-assess the content they create but also evaluate the suggestions provided
by the computational agent, deciding whether any of them are good enough to be
accepted. As for the computational agent, the internal evaluation it performs
to generate suggestions remains unclear. We lack insight into the
considerations that the AI models of the system have regarding the
modifications they propose and how these suggestions may enhance the current
level. Even if the system does conduct some form of evaluation, it cannot
express it. Therefore, it is the human agent who guides the level design
process, retaining complete control over the desired outcome for the creative
artifact.

Final decision: It is up to the human agent to decide when they are content
with the current level and ready to finish the creative process.

Task assignment: Both agents are working on the shared task of designing a
level for the Sokoban game. The contributions made by the human agent directly
influence the suggestions presented by the computational agent. Conversely,
the creative input from the computational agent may or may not affect the
creative process carried out by the human co-creator. Lastly, even though the
system cannot directly edit the grid of the current level, both agents
contribute by adding or altering tiles.

Intervention pace: The intervention pace of the computational agent is highly
dependent on the values of the parameters “step” and “tool radius”. When the
“step” value is low and the “tool radius” is small, the feedback of the system
is nearly instantaneous. However, if these values are increased, the system’s
response time also increases. Nevertheless, we consider this waiting time to
be reasonable. Both the human and the computer edit one or a few tiles at a
time, resulting in small content leaps. Consequently, the agents are not
overwhelmed by a large amount of new information all at once, allowing them to
gradually build the level and merge their ideas more easily.

Explainability: There is no explainability.

####  4.2.5. miWFC

Initial setting: In this case, the conceptual space is not restricted to any
specific game genre, and the tool can prove useful for creating different
kinds of 2D assets beyond maps. The computational agent offers a wide range of
templates that the human agent can choose from, but the human agent also has
the option to add their own custom templates. Despite the computer offering
several alternatives for the input that will serve as the basis to generate
the image, it is ultimately the human designer’s decision on which one to use.
The co-creative process can be initiated by the human agent by manually
creating a desired area on the canvas and then requesting the computational
agent to continue filling in the remaining space. Alternatively, the
computational agent can start generating content from a blank canvas, but only
upon request from the user. As such, although both agents can contribute to
the initial concept, it is the human agent who retains more control and
determines the specific problem they want to address.

Initiative: The human agent holds more initiative throughout the creative
process. The extent of contributions from the computational agent can vary
significantly depending on how much the human designer allows it to intervene.
Although the computational agent is practically capable of generating the
entire creative artifact, it can only do so with permission from the human
agent. Hence, the computational agent is not proactive. Furthermore, the
system aims to generate an image similar to the initial input and aligned with
the tile properties adjusted by the user. It does not actively seek to create
content that is novel and surprising, thus being less creative than the human
co-creator.

Evaluation: When the human agent is manually editing the canvas, the
computational agent assesses the tile or set of tiles that the human partner
intends to insert and may reject the edit if it deems it unfitting. However,
this evaluation performed by the system focuses on the accuracy with respect
to the example input provided for the generation, rather than the creativity
of the human agent’s contribution. In contrast, the human agent continuously
evaluates the quality of the content created by both themself and the
computational agent, revisiting previous stages and making changes if deemed
necessary. Thus, the evaluation process is primarily conducted by the human
agent.

Final decision: The human designer is the one who decides when they are
satisfied with the created map or texture and ready to export it.

Task assignment: The agents are involved in the same task of creating a 2D
visual asset, such as a game level or texture. Both the human agent and the
computational agent can directly edit the entire area of the bitmap, and their
contributions strongly influence each other. However, there are some actions
that the human agent can perform but the computational agent cannot.
Specifically, the human agent can delete content generated by the system and
manipulate tile properties.

Intervention pace: The co-creators have well-defined shifts. When the human
agent is editing the image, the computational agent waits and only intervenes
upon the human partner’s request. Despite this, the computational agent can
generate content immediately, enabling rapid interaction between the agents.
On the other hand, for the human designer to manually edit the canvas, they
must switch screens, inevitably slowing down the intervention pace. The amount
of content produced by each agent during their turn varies according to how
the human agent chooses to interact with the computational agent. The
computational agent can generate anything from a single tile to a complete
bitmap, depending on the extent to which the human partner allows.

Explainability: There is little explainability between the human agent and the
computational agent. All the user knows is that the system attempts to create
content similar to the template used as an example and according to the tile
properties defined by the user. Additionally, the system’s display of an
animation of the generative process that the user can analyze step by step
allows for a better understanding of the creative process of the computational
agent. Nevertheless, the human agent has no other means of gaining further
insight into the computational agent’s decisions, nor does the computational
agent have a way to comprehend the creative objectives and preferences of the
human agent.

####  4.2.6. Pitako

Initial setting: At the onset of the creative process, the system consistently
presents a default background sprite and three default suggestions. However,
the human agent can immediately add a new sprite, triggering further
suggestions from the computational agent. Consequently, while the human
designer may be influenced and even choose to accept the initial suggestions,
they retain control over the addition of the first sprite that truly begins to
shape the game.It is worth noting that the sprites available for the human
agent to add are limited to the behaviors and images found within the system’s
catalog. While the catalog is extensive, it still imposes creative limitations
on the human partner. Considering all these factors, we posit that the initial
setting criterion leans more toward the computer end of the spectrum.

Initiative: There are several actions that the human agent can perform. They
can manually add sprites, accept sprite suggestions, manipulate various
properties of each sprite, define interactions, accept interaction
suggestions, position sprites on the game map, and define rules for game
completion. On the other hand, the computational agent can only provide
recommendations for sprites and interactions. Regarding sprite suggestions,
the system autonomously provides them whenever the human designer adds a new
sprite to the set. However, there comes a point when the sprite set for the
developing game becomes extensive, and the system can no longer generate new
suggestions based on that sprite set. As for interaction suggestions, these
are presented only upon explicit user request. Thus, the human agent possesses
considerably greater initiative than the computer agent. The sprite suggestion
is the only part of the process where the computer is proactive, but even in
this case, its contribution is limited in creativity as it relies on
associations from existing games.

Evaluation: The evaluation is primarily conducted by the human designer, who
assesses their contributions and the game they are designing, as well as the
system’s recommendations. The computational agent solely self-evaluates its
suggestions in terms of “confidence” and communicates this assessment to the
human partner. A high confidence value indicates that the suggested element is
common in existing games, while a low confidence value indicates that it is
less common, making the suggestion more novel. However, the computational
agent does not conduct that same evaluation for the sprites and interactions
added by the user.

Final decision: The human agent is the one who determines when they are
satisfied with the game, the chosen sprites, and the implemented dynamics.

Task assignment: The agents share some tasks in the creative process, but not
all. Specifically, both are involved in creating sprites, mechanics, and
dynamics. The human agent can directly add these elements to the game, whereas
the computational agent only presents them as recommendations. Furthermore,
only certain actions by the human creator directly impact the next
contribution from the computer (e.g., adding a sprite with a behavior already
present in the set does not trigger new suggestions). In the case of sprite
placement on the level grid and the definition of termination rules, the
computational agent does not contribute to these tasks.

Intervention pace: The pace and spontaneity of co-creators’ interventions
depend on whether they are working on creating sprites or interactions. In the
case of sprites, the turns are short, with the computational agent typically
providing suggestions after each intervention by the human partner. The
content leap is not too large since both parties focus on one sprite at a
time. However, creating a sprite involves not only defining its graphical
representation but also its behavior, which, in turn, may require specifying
several properties. Thus, we can neither consider the content leap to be small
in this context. In the case of interactions, the system does not respond
spontaneously to the contributions of the human creator, providing
recommendations only upon request and solely based on the sprite set.
Consequently, the user receives no feedback as they add interactions to the
game.

Explainability: Explainability is almost non-existent. The computational agent
provides a small hint regarding the novelty of its recommendations by
displaying the confidence level, leaving it to the discretion of the human
partner whether they want to create a game that closely resembles existing
ones or opt for a set of sprites and interactions that is more distinct.
However, it does not provide any further information about its creative
process, and likewise, the human agent cannot justify their contributions
either.

####  4.2.7. Story Designer

Initial setting: In any case, the co-creative process begins with the system
consistently presenting the same base graph, as well as immediately providing
a set of suggestions. Consequently, the human agent is inevitably prompted to
shape the narrative structure around this initial content. While it is
possible for the human designer to erase the entire graph and start anew, the
initial setting presented by the computational agent is most likely to
influence on their creative process. Regarding the conceptual space, although
the nodes encapsulate a high degree of abstraction that allows for numerous
creative possibilities to elaborate the narrative afterward, they are
nonetheless rooted in tropes. Hence, they remain considerably constrained by
the narrative conventions commonly prevalent in existing games as identified
by the system.

Initiative: During the co-creative process, the human agent retains the
capability to directly edit the narrative graph, whereas the computational
agent provides suggestions prompted by human interventions without explicit
request. Although the computational agent contributes autonomously, with a
focus on presenting novel and interesting content, it remains unable to
manipulate the main graph. Furthermore, each suggestion only becomes visible
if the user opts to inspect it. As a result, the system might offer multiple
suggestions that the user may never come across. For these reasons, the human
agent has the stronger initiative.

Evaluation: Both parties evaluate the creative artifacts. The human agent
self-assesses the content they create and the suggestions presented by the
computational agent. Conversely, the computational agent evaluates both the
main graph and the suggestions it generates in terms of feasibility, while
also considering other metrics such as interestingness and coherence.
Additionally, the computational agent discloses its assessment of the main
narrative graph, the hovered suggestion, and the edited suggestion by
displaying fitness, interestingness, and coherence scores for each. However,
regardless of the computational agent’s evaluation of the quality and
creativity of the artifacts, only the human agent holds the authority to
accept suggestions if they prefer them over the current graph.

Final decision: It is the human agent who determines when the design of the
narrative structure is complete.

Task assignment: Both agents are engaged in the design of narrative structures
and can produce the same type of content - narrative graphs. However, the
human agent can manipulate the main graph by editing nodes and connections
individually, whereas the computational agent contributes by presenting
suggestions for complete graphs. The interventions made by the human agent
directly influence the subsequent input of the computational agent, but the
reverse may or may not occur.

Intervention pace: The system is not swift enough to generate suggestions in
real-time as the user makes alterations, often resulting in delayed responses.
Moreover, to view the suggestions generated by the computational agent, the
human partner needs to inspect them one by one, thereby slowing down the
intervention pace. As for the extent of the content leap, it varies from
suggestion to suggestion. For instance, suggestions with a higher value in the
“step” dimension will entail a more significant content leap.

Explainability: Similar to the Sentient Sketchbook, the system displays the
fitness, interestingness, and coherence scores of suggestions in relation to
the main narrative graph. This provides some insight into the specific
optimizations certain suggestions offer and why they might be preferable.
However, this level of explainability remains insufficient, and there is no
means for the human agent to reciprocally provide justification.

####  4.2.8. 1001 Nights

Initial setting: The human agent initiates the creative process by being the
first to write, setting the starting point from which the story will unfold.
Nevertheless, the computational agent still exerts some influence on the
initial setting. In addition to continuing the player’s narrative, the AI
model is prompted to focus on an ancient Middle Eastern story related to the
keywords representing all valid weapons. This, coupled with the game’s own
narrative setting and the goal of generating these weapons, steers the story
toward this particular literary genre.

Initiative: Both the human and the computational agent exhibit a strong
initiative as they contribute equally by writing chunks of the narrative
alternately. They are both attentive and adapt to each other’s input in a
creative manner. The ability to directly craft the story through writing
engages both participants deeply in the creative process. The only difference
lies in the human co-creator having a limited number of characters for their
writing turn, which allows the system to generate more extensive content.

Evaluation: Both agents assess what the other has written, choosing to adapt
to their colleague’s storyline and further develop their idea or change the
direction or topic of the narrative. However, the human agent can assess the
story as a whole. In contrast, the computational agent can only recall the
last five inputs from both itself and the human co-creator. While they respond
to each other’s input thoughtfully, neither agent can critique the creativity
and quality of the contributions from their partner.

Final decision: Since the narrative creation serves as a means to generate
game mechanics, there is no aim or incentive for the story to reach a
conclusion (unlike when storytelling itself is the primary goal). Nonetheless,
it is the human agent who determines when they are ready to engage in battle
with the King, thus concluding the co-creative storytelling session.

Task assignment: Both the human and the computational agent are performing the
same task and in the same manner. The creative work of one agent directly
affects what their partner will write next.

Intervention pace: In this approach, the agents have precise turns where each
one contributes a portion of the story alternately. Although the turns are
relatively short, we consider the content leap to be moderate, especially on
the side of the computational agent, as it can generate more content in its
turn. While the human agent may write one or two sentences, the computational
agent can write an entire paragraph uninterrupted. The intervention pace would
be faster if the agents could react while their counterpart is writing.

Explainability: Since the game’s objective is for the player to prompt the
King to mention keywords that can be converted into weapons, the player may
sometimes become puzzled when the system’s response lacks any of these
keywords. In such cases, the system provides the player with a hint that they
should tell a story more centered around swords and magic. However, this
explanation remains identical for every instance in which the King fails to
mention any of the items and proves ineffective if the player is already
narrating a story focused on these topics. Furthermore, this explanation only
vaguely alludes to the underlying process of the computational agent
concerning the use of target words, providing no further insight into its
overall creative process in story writing. Consequently, explainability is
almost absent.

####  4.2.9. Germinate

Initial setting: The Germinate tool is tailored for a style of gameplay with
simple mechanics, yet it still allows for the expression of specific
rhetorical intent. Specifically, the games created with this tool can only be
played with the mouse, lack a game map, and offer limited variety in the types
of dynamics possible between entities and resources. Consequently, part of the
conceptual space is constrained by the computational agent. However, the
meaning assigned to the game, its goals, rules, and interactions are yet to be
defined. Typically, it is the human agent who establishes the initial rule
set. The system provides some example cards to get things started, but it is
up to the user to determine which entities, resources, relationships, and
triggers they want to add. The human agent can also request the computational
agent to generate a random card, but ultimately, the initial concept of the
game is mostly determined by the human agent.

Initiative: Once the human agent specifies their intention by defining the
cards, the computational agent generates a batch of complete and playable
games, each with its established rules and dynamics. If the human co-creator
has allowed it beforehand, the computational agent can also generate entities
and resources for each game beyond those defined by the human partner.
Throughout the co-creative process, the system recurrently generates more
batches of games for the user to explore. After the human agent makes changes
to the properties and requests the computational agent to generate a new set
of games, the current game list is cleared and the computational agent starts
generating new games based on the updated settings. In this scenario, we
consider the computational agent to have more initiative as it can
continuously create various complete games, whereas the human agent can only
define cards and cannot directly edit any game. Initially, the computational
agent may not be proactive, but once prompted by the human agent, it
autonomously generates games. Furthermore, despite the games generated by the
system being constrained by the user’s expressed intent, the system can
typically add a fair amount of content to the game beyond what the user
established. Therefore, the computational agent still retains a significant
degree of creative freedom.

Evaluation: The evaluation is primarily conducted by the human agent. While
the computational agent can generate numerous games, it is the human agent who
explores them to identify any with desirable characteristics. Based on this
assessment, the human agent may modify their intent, leading to the discarding
of all previous games to generate a new list. The only assessment made by the
system is to check for contradictions in the cards defined by the user that
would prevent game generation, without specifying the nature of these
contradictions. Importantly, the computational agent does not provide any
input or judgment on the meaning and creativity of the rhetorical intent of
the human co-creator.

Final decision: The human agent is the one who decides whether they are
satisfied with any of the games and when the creative process comes to an end.

Task assignment: Both agents are focused on creating a game that expresses
specific rhetorical goals. However, they cannot engage in the same type of
actions in the co-creative process. While the computational agent is capable
of generating complete games, the human agent can only define cards in a way
that, hopefully, their intent is understood and conveyed in the games based
solely on these guidelines. The contributions of the computational agent do
not directly affect the subsequent actions of the human agent.

Intervention pace: Each agent has their own defined shift: the human agent is
responsible for setting the cards, while the computational agent generates
batches of games. These shifts tend to be lengthy. The system takes a
considerable amount of time to generate the first batch of games, leaving the
user waiting without any feedback during this period. Due to the extended
duration of the computational agent’s turn, it is likely that the human
agent’s turn will also be long, as they may choose to make multiple
modifications at once. Moreover, as the content being created transitions from
properties described in cards to full games generated by the system, the
content leap is significant.

Explainability: This approach lacks extensive explainability, though it offers
more than the previous works. In addition to using cards to express their
intentions, the human co-creator can provide further clarification by adding
tags to entity or resource cards. For instance, they may include a “Stress”
resource with the tag “player thinks it’s bad”, enabling the computational
agent to better grasp their vision. Conversely, each game generated by the
computational agent includes its rules displayed alongside in the form of
cards, similar to the ones used by the human agent to define their intent.
However, the presentation of these cards alone is often insufficient, and the
human agent still struggles to comprehend the exact rules of the game and the
message it aims to convey. A significant issue with this tool lies in its lack
of explainability when the system detects inconsistencies in the user’s intent
and is unable to generate games. In such cases, the system merely states that
it cannot generate the games, without explaining the existing contradictions.
This hinders the co-creative process, leaving the user unaware of what changes
are necessary to resolve the issue.

####  4.2.10. Overview

As previously mentioned, this is not an exhaustive analysis, and different
individuals may arrive at distinct conclusions regarding the level of MI-CCy
in each tool. Nevertheless, it enables us to compare the approaches and
identify relevant features in some that may be lacking in others.
Additionally, it enables us to pinpoint characteristics common to most
approaches, which can be addressed when developing future MI-CCy approaches in
game design. In Figure 11, we provide an overview classification of each tool
based on the MI-CCy Quantifier criteria. We can observe that the works that
stand out with a higher level of MI-CCy are the Sentient Sketchbook and 1001
Nights. By examining Figure 11 alone, several patterns become discernible.
Specifically, the initial setting (i.e., defining the problem at hand, the
conceptual space, and the starting point) tends to be skewed towards one of
the agents. The evaluation of the process, artifacts, and creativity of the
agents is primarily conducted by the human co-creator. Additionally, the final
decision is always up to the human agent. Lastly, there is a general lack of
explainability, both from the computational agent to the human agent and vice
versa.

As mentioned earlier, game design is a broad discipline, so these approaches
can address several types of game content at different complexities.
Furthermore, there are a variety of techniques that can be used depending on
the goals and intended functionality. We can identify some trends in the
techniques employed in these MI-CCy approaches. Evolutionary algorithms are
among the most commonly used techniques in this context. For instance, the
Sentient Sketchbook utilizes genetic algorithms, while both the AI Spaceship
Generator and Story Designer employ MAP-Elites. The use of machine learning
algorithms is also prevalent, as seen in the RL Brush, which relies on
reinforcement-learning-based models, and the Sprite Editor for Pixel Art
Characters, which uses generative adversarial networks. 1001 Nights makes use
of Natural Language Processing, a technique often seen in co-creative writing
tools. Among the tools we have analyzed, there are also some less conventional
content generation techniques. That is the case for the miWFC, which applies
the Wave Function Collapse algorithm, and Germinate, which makes use of answer
set programming to generate games. The Pitako tool also stands out for its
recommendation-based approach through frequent itemset mining.

The survey conducted by Lai et al. provides a more extensive review of the
techniques used in mixed-initiative systems for game content creation. From
our analysis, we contend that the underlying algorithms in these systems do
not have as significant an impact on the level of MI-CCy as the means through
which agents can contribute with creative input and communicate with one
another. Nonetheless, we observe that techniques that encourage the
computational agent to generate more novel and interesting content enable it
to play a more significant role in the process.

In addition to the algorithms and techniques used, it is crucial to overview
how agents interact within the creative process. Regarding the computational
agent, it is frequent for their contributions to involve presenting a set of
suggestions from which the human agent can choose to accept one. This trend is
observed in six out of the nine systems assessed using the MI-CCy Quantifier.
Only in two works, namely miWFC and 1001 Nights, does the computational agent
directly add content to the main creative artifact. Finally, in the Sprite
Editor for Pixel Art Characters, the computational agent offers a single
suggestion at a time for the user to accept or reject. Conversely, the human
agent can add content directly to the final artifact in seven out of the nine
frameworks, underscoring a clear distinction from their computational partner.
The only exceptions are the AI Spaceship Generator, where the human agent is
limited to selecting suggestions, and Germinate, where they can define cards
instead of directly designing the game.

Figure 11. Overview of the analysis of the degree of mixed-mixed-initiative
co-creativity (MI-CCy) for each work using the MI-CCy Quantifier.

##  5\. Discussion and Open Issues

The works presented in Section 3 and further explored in Section 4.2 express a
fair sample of the current progress in the development of mixed-initiative
approaches to game design. Several other works were analyzed but not elected
as use cases for this tutorial. Despite the diversity of recent works, there
are some open issues. Below we describe the main ones identified:

##### Coarse collaboration

Most approaches still fail to promote seamless collaboration between the human
and the computer. In most cases, the human and the computational agent are
still performing separated steps at a time and, therefore, not learning much
from each other. For instance, in Germinate (Kreminski et al., 2020), the user
sets the initial properties, and then the computer generates games according
to those defined properties. Subsequently, the user can analyze the generated
content and make changes to the properties, followed by the computer
generating more games. Various steps of modifying properties according to the
new knowledge acquired from the output can result in a reasonable co-creative
process. However, the rigid alternating of tasks remains noticeable, and the
agents have a hard time engaging in a productive exchange of ideas that allows
for the ideas to be smoothly blended.

##### Unbalanced partnership

There is an unbalanced amount of initiative and creative input between the
human and the computational agent. It is observable that some works favor
computational initiative, while others favor human initiative. In the work of
Gallotta et al. (Gallotta et al., 2023) the computational agent generates the
first population of spaceships and has almost full control over what is
presented. The human counterpart has low initiative and creative input as they
can only select spaceships throughout the process. In contrast, in the Sprite
Editor for Pixel Art Characters (dos Santos Coutinho and Chaimowicz, 2023) the
computer is not as creative as the human, as the human partner creates the
base character design, and the system always tries to match their vision
during the course of co-creation. Compounding this issue, there are generally
few instances where the computational agent is completely spontaneous, i.e.,
cases where it can react without requiring any trigger from the human
counterpart. With the exception of when the computer presents an initial
setting, if the human agent does not take any action, the computer remains
inactive as well.

##### Poor diversity of game content

There is a lack of research in some domains of game design. Level design is
the type of game content most tackled by current approaches. Although
relevant, the high focus on map creation results in other critical aspects of
game design receiving less consideration. Game content such as characters,
sound, user interface, or complex interactions between game elements tend to
be overlooked. Narrative design and the creation of visual assets, such as 2D
or 3D models, although prevalent in many co-creative tools across broader
domains, do not receive as much attention within the specific realm of video
games.

##### Hardly generalizable

The frameworks tend to be devised with a specific game genre and exploration
space in mind, thus being difficult to generalize. In existing mixed-
initiative tools, those focused on level design are typically restricted to a
map style, such as dungeon crawler or platformer. Moreover, the visual
aesthetics are often limited to what the framework provides, and the potential
to expand it to other themes or art styles is unclear. Likewise, other
content, such as narrative, mechanics, and interactions between game elements,
are generally limited to a narrow solution space. An example is the level
design approach in RL Brush (Delarosa et al., 2021), which is constrained to
the particular design space of the Sokoban game. Consequently, these
approaches are only suited for designing highly specific types of games.

##### No explainability

Explainability is either nonexistent or severely limited. Few tools provide
two-way communication channels between the agents involved in the creative
process. In most cases, neither the human agent nor the computational agent
has the means to explain and defend their ideas and contributions. In
instances where the system does attempt to provide some form of explanation,
it is typically confined to presenting some metric scores. If co-creative
partners cannot comprehend each other’s intentions and motivations, it becomes
more challenging to converge their ideas and seamlessly integrate them into
the final artifact.

##### Undefined evaluation methods

Further research is required to establish appropriate methods for evaluating
and validating these approaches. The evaluation of mixed-initiative approaches
is a complex matter, given the multiple facets that must be considered,
including the level of collaboration provided, the ability to foster
creativity, and the quality and creative value of the content resulting from
the design process. Nevertheless, this evaluation is necessary to rigorously
compare different approaches and measure the progress made in the field.

We recommend that individuals interested in developing novel mixed-initiative
co-creative tools for game design should assess whether their system addresses
any of these gaps. The MI-CCy Quantifier can serve as a valuable tool for
evaluating how well your developing system performs against each criterion and
what improvements it brings to the current state-of-the-art. We note that the
MI-CCy Quantifier is not a closed framework, meaning that additional
dimensions can potentially be incorporated beyond the ones we have discussed
here. Usability is one such example. A user-friendly interface is crucial for
facilitating interaction between the human and computational agents. While
testing these tools, we observed variations in usability, with some
demonstrating superior usability compared to others. This directly impacted
our willingness to engage with the system and the quality of the co-creative
process. A tool with poor usability generally results in a lower level of co-
creativity.

##  6\. Conclusion

In this paper, we provided guidance to researchers, developers, and game
designers interested in implementing new approaches to game design focused on
a high level of mixed-initiative co-creativity. To do so, we presented
existing works on MI-CCy in game design targeted at several classes of game
content. We introduced the MI-CCy Quantifier, a directive framework for
analyzing works based on their level of MI-CCy in a descriptive and visually
quantifiable manner. We illustrated its usage by applying it to nine of the
presented frameworks. Through this analysis, we identified patterns in current
approaches and characteristics that favor some over others. Furthermore, we
highlight prevalent open issues in the state-of-the-art, which we propose as
key points to address when conceiving new MI-CCy frameworks for game design.
In particular, we recommend that people working on forthcoming approaches
focus on promoting a seamless and balanced partnership between the human agent
and the computational agent. We also emphasize the need to address overlooked
types of game content, to aim for greater versatility, and to enhance
explainability. Finally, it is crucial to establish methods for evaluating MI-
CCy approaches.

###### Acknowledgements.

This work is funded by the FCT – Foundation for Science and Technology,
I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R&D Unit
– UIDB/00326/2020 or project code UIDP/00326/2020. The first author is funded
by Foundation for Science and Technology (FCT), Portugal, under the grant
2021.07047.BD.

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