This article comprises Chapter 11 of the book "Procedural Content Generation in Games: A Textbook and an Overview of Current Research" (edited by Noor Shaker, Julian Togelius and Mark J. Nelson). Information about the book, along with guidelines for citing it, can be found here. The text and images of the article have been adapted for online viewing; a few corrections have also been made.
Algorithms can generate game content, but so can humans. And while PCG algorithms can generate some kinds of game content remarkably well and extremely quickly, some other types (and aspects) of game content are still best made by humans. Can we combine the advantages of procedural generation and human creation somehow? This chapter discusses mixed-initiative systems for PCG, where both humans and software have agency and co-create content. A small taxonomy is presented of different ways in which humans and algorithms can collaborate, and then three mixed-initiative PCG systems are discussed in some detail: Tanagra, Sentient Sketchbook, and Ropossum.
Many PCG methods discussed so far in this book have focused on fully automated content generation. Mixed-initiative procedural content generation covers a broad range of generators, algorithms, and tools which share one common trait: they require human input in order to be of any use. While most generators require some initial setup, whether it's as little as a human pressing "generate", or providing configuration and constraints on the output, mixed-initiative PCG automates only part of the process, requiring significantly more human input during the generation process than other forms of PCG.
As the phrase suggests, both a human creator and a computational creator "take the initiative" in mixed-initiative PCG systems. However, there is a sliding scale on the type and impact of each of these creators' initiative. For instance, one can argue that a human novelist using a text editor on their computer is a mixed-initiative process, with the human user providing most of the initiative but the text editor facilitating their process (spell-checking, word counting or choosing when to end a line). At the other extreme, the map generator in Civilization V (Firaxis 2014) is a mixed-initiative process, since the user provides a number of desired properties of the map. This chapter will focus on less extreme cases, however, where both human and computer have some significant impact on the sort of content generated.
Computer-Aided Design: humans have the idea, the computer supports their creative process.
Interactive Evolution: the computer creates content, humans guide it to create content they prefer.
It is naive to expect that the human creator and the computational creator always have equal say in the creative process:
To understand how mixed-initiative PCG systems are designed today, as well as to attain future inspiration for such systems, it is important to also understand several older systems from which current work draws inspiration. There are three main threads of work that we'll look at in this section: mixed-initiative interaction, computer-assisted design, and creativity support tools. Each of these areas of work have inspired different aspects of the systems we'll talk about in the chapter.
In 1960, J.C.R. Licklider  laid out his dream of the future of computing: man-computer symbiosis. Licklider was the first to suggest that the operator of a computer take on any role other than that of the puppetmaster - he envisioned that one day the computer would have a more symbiotic relationship with the human operator. Licklider described a flaw of existing interactive computer systems as: "In the man-machine systems of the past, the human operator supplied the initiative, the direction, the integration, and the criterion."
Notice the use of the term "initiative" to refer to how the human interacts with the computer, and the implication that the future of man-computer symbiosis therefore involves the computer being able to share initiative with its human user.
The term "mixed-initiative" was first used by Jaime Carbonell to describe his computer-aided instruction system, called SCHOLAR . SCHOLAR is a text-based instructional system that largely consisted of the computer asking quiz-style questions of the student using the system; the mixed-initiative component of the system allows the student to ask questions of the computer as well. Carbonell argued that there were two particularly important and related aspects of a mixed-initiative system: context and relevancy. Maintaining context involved ensuring that the computer would only be able to ask questions that were contextually relevant to the discussion thus far, so that large sways in conversation did not occur. Relevancy involves only answering questions with relevant information, rather than all of the information known about the topic.
Mixed-initiative interaction describes a form of interaction between computer and human in which initiative is shared. It can be helpful to think about this style of interaction as a conversation-imagine, for example, two human colleagues having a conversation in the workplace:
There are several ways in which Kevin and Sarah are sharing initiative in this conversation. Novick and Sutton  describe several components of initiative:
The majority of mixed-initiative PCG systems focus entirely on the second kind of initiative: speaker initiative. They involve the computer being able to provide support during the design process, an activity that design researcher Donald Schön has described as a reflective conversation with the medium  (more on this in the next section). However, they all explicitly give the human designer sole responsibility for determining what the topic of the design conversation will be and how to solve the problem; all mixed-initiative PCG systems made thus far have prioritized human control over the generated content.
Doug Engelbart, an early pioneer of computing, posed that computers stand to augment human intellect. He envisioned a future in which computers were capable of "increasing the capability of a man to approach a complex problem situation, to gain comprehension to suit his particular needs, and to derive solutions to problems" . Engelbart argued that all technology can serve this purpose. His de-augmentation experiment, in which he wrote the same text using a typewriter, a normal pen, and a pen with a brick attached to it, showed the influence that the technology (in this case, the way we write) has on the ways that we write and communicate.
A peer of Engelbart, Ivan Sutherland's created the Sketchpad system in 1963 . This was the first system to offer computational support for designers; it was also the first example of an object-oriented system (though it did not involve programming). Sketchpad allowed designers to specify constraints on the designs they were drawing; for example, it was possible to draw the general topology of an item such as a bolt. The user could then place constraints on the edges of the bolt to force them to be perpendicular to each other. The system was object-oriented in that individual sketches could be imported into others to produce entire diagrams and drawings; if the original sketch was altered, that change would propagate to all diagrams that imported the sketch. The idea of letting users create through adding and removing constraints has carried forward into mixed-initiative tools such as Tanagra and Sketchaworld, described later in this chapter.
A decade after Engelbart and Sutherland's work, Nicholas Negroponte proposed the creation of design amplifiers. Negroponte was particularly interested in how to support non-expert designers, as he was concerned that experts often push their own agendas without regard for the need of the occupant . However, home-owners do not have the domain expertise required to design their own home. His vision was for tools that could help the general population in creating their own homes using the computer to support their designs and ensure validity of the design. The idea that human creators should take the forefront and have the majority of control over a design situation is reflected in all mixed-initiative design tools; in general, the computer is never allowed to override a decision made by the human. However, all tools must push an agenda to some extent, though it may not be intentional: the choices that go into how the content generator operates and what kind of content it is capable of creating vastly influences the work that the human designer can create with the system.
More recently, Chaim Gingold has pushed the idea of "magic crayons": software that supports a novice's creativity while also being intuitive, powerful, and expressive . Gingold argues that using design support tools should be as simple and obvious as using a crayon, and allow for instant creativity. Any child who picks up a crayon can quickly and easily grasp how to use it and go on to create several drawings quite rapidly. The "magic" part of the magic crayon comes in the crayon's computational power and expressive potential: the crayons are imbued with computational support that allows a user to create something better than what they would normally create themselves, while still echoing their original design intent.
When designing a mixed-initiative system, there are several main questions to consider. These points are based on the authors' experiences creating their own prototype mixed-initiative tools:
How to design both the underlying technology and the interface for a mixed-initiative system depends wildly upon who the target audience is. A tool for professional designers might look considerably different than a tool intended for game players who have no design experience.
A mixed-initiative environment offers the opportunity for more sophisticated level editing operations than merely altering content as one could do in a non-AI supported tool. The way the generation algorithm works might prioritize certain aspects of the design. For example, Tanagra's underlying generator used rhythm as a driver for creating levels; thus, it was relatively straightforward to permit users to interact with that underlying structure to be able to directly manipulate level pacing.
Mixed-initiative content generators can involve both direct and indirect manipulation of the content being created. For example, the tool will typically support a user directly drawing in aspects of the content (e.g. level geometry), but also allow the computer to take over and make new suggestions for the generator. How to balance these forms of control can be challenging (especially when the human and computer conflict, see next point). Should the computer be allowed to make new suggestions whenever it wants, or only when specifically requested? How much of the content should be directly manipulable?
In situations where both human and computer are editing content simultaneously, editing conflict inevitably arises. The majority of mixed-initiative tools follow the principle that the human has final say over what is produced by the tool. However, when the human user states contradictory desires, the system must decide how to handle the situation. Should it simply provide an error message? Should it randomly choose which desire is more important for the human? Should it generate several plausible answers and then ask the human to choose which solution is most reasonable?
More generally, the issue is: how can the computer infer human design intent via an interface where the human simply interacts with the content itself.
All content that a human can produce using a mixed-initiative PCG system must be possible for the computer to generate on its own. Thus it is vital for the system to be expressive enough to offer a meaningful set of choices to the human user.
It is difficult for a human and computer to engage in a design collaboration if neither is able to explain itself to the other. In particular, a human designer may become frustrated or confused if the computer consistently acts as though it is not following the model that the human designer has in her head for how the system should work. The computer should appear intelligent (even if the choices it is making do not involve a sophisticated AI system), and ideally should be able to explain its actions to the human. Being able to communicate at a meta-level about the design tasks and outcomes has not been well-explored in mixed-initiative PCG work thus far.
Although not comprehensive, the following Computer-Aided Design tools do not only afford interaction and feedback for the human designer, but also introduce some initiative (in varying degrees and in different forms) to the computational designer.
The Tanagra intelligent level design tool. The large area in the upper left is where the level is created. Below that is a beat timeline, allowing manipulation of the pacing of the level. On the right are buttons for editing the level.
Tanagra is a mixed-initiative tool for level design, allowing a human and a computer to work together to produce a level for a 2D platformer . An underlying, reactive level generator ensures that all levels created in the environment are playable, and provides the ability for a human designer to rapidly view many different levels that meet their specifications. The human designer can iteratively refine the level by placing and moving level geometry, as well as through directly manipulating the pacing of the level. Tanagra's underlying level generator is capable of producing many different variations on a level more rapidly than human designers, whose strengths instead lie in creativity and the ability to judge the quality of the generated content. The generator is able to guarantee that all the levels it creates are playable, thus refocusing early playtesting effort from checking that all sections of the level are reachable to exploring how to create fun levels.
A combination of reactive planning and constraint programming allows Tanagra to respond to designer changes in real-time. A Behavior Language (ABL)  is used for reactive planning, and Choco  for numerical constraint solving. Reactive planning allows for the expression of generator behaviors, such as placing patterns of geometry or altering the pacing of the level, which can be interleaved with a human designer's actions.
The version of Tanagra displayed above incorporates (1) the concept of a user "pinning" geometry in place by adding numerical positioning constraints, (2) the system attempting to minimize the number of required positioning changes (including never being allowed to move pinned geometry), and (3) direct changes to level pacing by adding, removing, and altering the length of beats. Later versions of Tanagra altered the UI to make it clearer what geometry was "pinned" and what was not. The latest version of Tanagra also added the idea of geometry preference toggles, allowing designers an additional layer of control over the system by letting them state whether or not particular geometry patterns are preferred or disliked on a per-beat basis. More information about the Tanagra tool, including a full description of its system architecture and an evaluation of its expressivity, has been published .
The user interface of Sentient Sketchbook as a human designer edits their sketch (left) and a generator, acting as the artificial designer, creates map suggestions to the user's sketch (far right).
The map sketch can be trans-formed into a high-resolution map.
Sentient Sketchbook is a computer-aided design tool which assists a human designer in creating game levels, such as maps for strategy games  or dungeons for roguelike games . Sentient Sketchbook uses the notion of map sketch as a minimal abstraction of a full game level; this abstraction limits user fatigue while creating new levels and reduces the computational effort of automatically evaluating such sketches. Like popular CAD tools, Sentient Sketchbook supports the human creator by automatically testing maps for playability constraints, by calculating and displaying navigable paths, by evaluating the map on gameplay properties and by converting the coarse map sketch into a playable level.
The innovation of Sentient Sketchbook is the real-time generation and presentation of alternatives to the user's sketch. These alternatives are evolved from an initial population seeded by the user's sketch, and thus a certain degree of map integrity is maintained with the user's designs. The shown suggestions are guaranteed to be playable (i.e. have all vital components such as bases and resources for strategy games connected with passable paths) via the use of constrained evolutionary optimization with two populations . The suggestions are either evolved to maximize one of the predefined objective functions inspired by popular game design patterns such as balance and exploration , or towards divergence from the user's current sketch through feasible-infeasible novelty search .
A screenshot from one of the interfaces in Ropossum. The components highlighted are the ones constrained by the designer and therefore will not be changed when evolving complete playable levels.
Ropossum is an authoring tool for the generation and testing of levels of the physics-based puzzle game, Cut the Rope . Ropossum integrates many features: (1) automatic design of complete solvable content, (2) incorporation of designer's input through the creation of complete or partial designs, (3) automatic check for playability and (4) optimization of a given design based on playability.
Ropossum consists of two main modules: an evolutionary framework for procedural content generation  and a physics-based playability module to solve given designs . The second module is used both for evolving playable content and for play testing levels designed by humans. The parameters of the evolutionary system and the AI agent were optimized so that the system can respond to the user's inputs within a reasonable amount of time. Grammatical Evolution (GE) is used to evolve the content. The level structure is defined in a Design Grammar (DG) which defines the positions and properties of different game components and permits an easy to read and manipulate format by game designers . First-order logic is used to encode the game state as facts specifying the game components and their properties (such as position, speed, and moving direction) . The relationships between the components are represented as rules used to infer the possible next actions.
The two methods for evolving game design and assessing whether the design is playable are combined in a framework to evolve playable content. An initial level design, according to the design grammar, is generated or created by the designer and encoded as facts that can be used by the AI reasoning agent. Given the game state, the agent infers the next best action(s) to perform. The actions are then sent to the physics simulator that performs the actions according to a given priority and updates the game state accordingly. The new game state is sent to the agent to infer the next action. If the sequence of actions does not lead to winning the level, the system backtracks. A state tree is generated that represents the actions and states explored. For each action performed, a node in the tree is generated and the tree is explored in a depth-first approach. The size of the explored branches in the solution tree was drastically reduced through the use of different priorities for the actions and the employment of domain knowledge encoded by the rules followed by the reasoning agent when inferring the best action to perform.
A screenshot from Sketchaworld showing the user input consisting of a sketch of a mountain, river and forest and the corresponding 3D terrain generated.
Sketchaworld is an interactive tool created to enable a non-specialist user to easily and efficiently create a complete 3D virtual world by integrating different procedural techniques . Sketchaworld integrates many features: (1) it facilitates easy interaction with designers who can specify procedural modeling operations and directly visualize their effects, (2) it builds 3D worlds by fitting all features with their surroundings and (3) it supports iterative modeling.
The tool allows user interactions in two main modes:landscape mode and feature mode. The first mode consists of the user input which is a 2D layout map of the virtual world formatted in a coloring grid that includes information about elevations and soil materials painted with a brush. In the feature mode, users add more specific land features such as cities and rivers.
While users sketch on the 2D grid, the effects of their modification is directly visualized in the 3D virtual world. This requires blending the features added with its surroundings. To this end, whenever a new feature is created, an automatic local adaptation step is performed to ensure smoothness and correctness. This includes for example removing trees on a generated road's path or adding a road to connect a generated bridge.
As its name suggests, Interactive Evolutionary Computation (IEC) is a variant of evolutionary computation where human input is used to evaluate content. As artificial evolution hinges on the notion of survival of the fittest, in interactive evolution a human user essentially selects which individuals create offspring and which individuals die. According to Takagi , interactive evolution allows human users to evaluate individuals based on their subjective preferences (their own psychological space) while the computer searches for this human-specified global optimum in the genotypical space (feature parameter space); as such, the collaboration between human and computer makes IEC a mixed-initiative approach. In interactive evolution a human user can evaluate artifacts by assigning to each a numerical value (proportionate to their preference for this artifact), by ordering artifacts in order of preference, or by simply selecting one or more artifacts that they would like to see more of. With more control to the human user, the artifacts in the next generation may match users' desires better; the user's cognitive effort may also increase, however, which results in user fatigue (more on this in the next section).
Interactive evolution is often used in domains where designing a fitness function is a difficult task; for instance, the criteria for selection could be a subjective measure of beauty as in evolutionary art, a deceptive problem where a naive quantifiable measure may be more harmful than helpful, or in cases where mathematically defining a measure of optimality is as challenging as the optimization task itself. Since it allows for a subjective evaluation of beauty, IEC has often been used to create 2D visual artifacts based on L-systems , mathematical expressions , neural networks  or other methods. Using interactive evolution in art was often motivated by a general interest in Artificial Life, as is the case with Dawkin's Biomorph . In evolutionary art, human users may often evaluate not the phenotypes but situational system outputs specified by the phenotypes, in cases where the phenotypes are image filters or shaders . Apart from 2D visual artifacts, IEC has also been used in generating 3D art , graphic movies , typographies  and graphic design . Evolutionary music has also used IEC to generate the rhythm of percussion parts , jazz melodies  or accompaniments to human-authored music scores , among others. Outside evolutionary art and music, IEC has been used for industrial design , image database retrieval , human-like robot motion  and many others. The survey by Takagi  provides a thorough, if somewhat dated, overview of IEC applications.
Since interactive evolution is entirely reliant on human input to drive its search processes, its largest weakness is the effect of human fatigue in human-computer interaction. Human fatigue becomes an issue when the users are required to perform a large number of content selections, when feedback from the system is slow, when the users are simultaneously presented with a large number of content on-screen, or when users are required to provide very specific input. All of these factors contribute to the cognitive overload of the user, and several solutions have been proposed to counteract each of these factors.
As human users are often overburdened by the simultaneous presentation of information on-screen, user fatigue can be limited by an interactive evolutionary system that shows only a subset of the entire population. There are a number of techniques for selecting which individuals to show, although they all introduce biases from the part of the tool's designers. An intuitive criterion is to avoid showing individuals which all users would consider unwanted. Deciding which individuals are unwanted is sometimes straightforward; for instance, musical tracks containing only silence or 3D meshes with disconnected triangles. However, such methods often only prune the edges of the search space and are still not guaranteed to show wanted content. Another technique is to show only individuals with the highest fitness; since fitness in interactive evolution is largely derived from user choices, this is likely to result in individuals which are very similar - if not identical - to individuals shown previously, which is more likely to increase fatigue due to perceived stagnation.
User fatigue is often induced when the requirement of a large number of selections becomes time-consuming and cumbersome. As already mentioned, fewer individuals than the entire population can be shown to the user; in a similar vein, not every generation of individuals needs to be shown to the user, instead showing individuals every 5 or 10 generations. In order to accomplish such a behavior, the fitness of unseen content must be somehow predicted based on users' choices among seen content. One way to accomplish such a prediction is via distance-based approaches, i.e. by comparing an individual that hasn't been presented to the user with those individuals which were presented to the user: the fitness of this unseen individual can be proportional to the user-specified fitness of the closest seen individual while inversely proportional to their distance . Such a technique essentially clusters all individuals in the population around the few presented individuals; this permits the use of a population larger than the number of shown individuals as well as an offline evolutionary sprint with no human input. Depending on the number of seen individuals and the expressiveness of the algorithm's representation, however, a number of strong assumptions are made - the most important of which pertains to the measure of distance used. In order to avoid extraneous bias of the search from these assumptions, most evolutionary sprints are only for a few generations before new human feedback is required.
Another solution to the extraneous choices required of IEC systems' users is to crowdsource the selection process among a large group of individuals. Some form of online database is likely necessary towards that end, allowing users to start evolving content previously evolved by another user. A good example of this method is PicBreeder , which evolves images created by compositional pattern-producing networks (CPPNs) . Since evolution progressively increases the size of CPPNs due to the Neuroevolution of Augmenting Topologies algorithm , the patterns of the images they create become more complex and inspiring with large networks. This, however, requires extensive evolution via manual labor, which is expected to induce significant fatigue on a single user. For that reason, the PicBreeder website allows users to start evolution "from scratch", with a small CPPN able to create simple patterns such as circles or gradients, or instead load images evolved by previous users and evolve them further. Since such images are explicitly saved by past users because they are visually interesting, the user starts from a "good" area of the genotypical space and is more likely to have meaningful variations rather than if they were starting from scratch and had to explore a large area of the search space which contains non-interesting images.
Another factor of user fatigue is the slow feedback of evolutionary systems; since artificial evolution is rarely a fast process, especially with large populations, the user may have to sit through long periods of inaction before the next set of content is presented. In order to alleviate that, interactive evolution addresses it by several shortcuts to speed up convergence of the algorithm. This is often accomplished by limiting the population size to 10 or 20 individuals, or by allowing the user to actively interfere directly on the search process by designating an estimated global optimum on a visualization of the search space .
To reduce the cognitive load of evaluating individuals, the most common solution is to limit the number of rating levels. In the simplest of cases, the rating levels can be two, i.e. selected and unselected, limiting the user's effort; they either like the content or they don't. Since taste is rarely a boolean value, however, usually more rating levels are included - often using existing scales such as that of "5 stars" which is popular in restaurant and cinema reviews. Another solution which is expected to limit the cognitive load and subjectivity of ratings is to use rankings , i.e. stating preference of A over B without explicitly specifying that A is 3 of 5 stars and B is 1 of 5 stars.
Recent trends have shown an increase in the use of IEC for evolving game content. As highly interactive experiences themselves, games are ideal for interactive evolution as the user's preference can be inferred based on gameplay traces. In this fashion, the selection of favored content is masqueraded into in-game activities such as shooting, trading or staying alive. Done properly, interactive evolution in games can bypass to a large extent the issue of user fatigue. However, the mapping between player actions and player preference is often not straightforward; for instance, do humans prefer to survive in a game level for a long time, or do they like to be challenged and be constantly firing their weapons? Depending on the choice of metric (in this example, survival time or shots fired), different content may be favored. Therefore, gameplay-based evaluations may include more biases on the part of the programmer than traditional interactive evolution, which tries to make no assumptions.
Galactic Arms Race with multiple players using different weapons.
Galactic Arms Race  is one of the more successful examples of games using interactive evolution. The procedurally generated weapon projectiles, which are the main focus of this space shooter game, are evolved interactively based on gameplay data. In Galactic Arms Race, the number of times a weapon is fired is considered an indication of preference; Hastings et al. assume that players who don't like a weapon will not use it as much as others. Weapon projectiles, represented as particles, are evolved via neuroevolution of augmenting topologies ; the velocity and color of each particle is defined as the output of a CPPN , with the input being the current position and distance from the firing spaceship. Newly evolved weapons are dropped as rewards for destroying enemy bases; the player can pick them up, and use them or switch among three weapons at any given time. Galactic Arms Race can be also played by many players; in the case of multiplayer, the algorithm uses the firing rates of all players when determining which weapons to evolve.
The user interface used to visualize and collect the ranking of tracks.
A more "traditional" form of interactive evolution - with a user directly stating preference of game content - was applied to generate tracks for a car racing game . The system uses the Open Racing Car Simulator (TORCS) and allows user interaction through a web browser where users can view populations of racing tracks and evaluate them. The other component of the system is an evolutionary backend which handles all evolutionary-based operations. Racing tracks are represented in the engine as a list of segments which can be either straight or turning. In the evolution process, a set of control points and Bezier curves were employed to connect these points and ensure smoothness.
Different variations of the interactive evolution method are implemented to evaluate the generated tracks. In the single-user mode, human subjects were asked to play 10 generations of 20 evolved tracks each and evaluate them using two scoring interfaces: like/dislike and rating from 1 to 5 stars. The feedback provided by users about each track is used as a fitness in the evolution process. In the multi-user mode, the same population of 20 individuals is played and evaluated by five human subjects. The fitness given to each track in the population is the average score received by all users. The feedback provided by users showed improvements in the quality of the tracks and an increase in their interestingness.
The user interface of the spaceship generator, where the user can select their favorite among a subset of the population of feasible spaceships.
The work of Liapis et al.  is an example of fitness prediction applied for the purpose of speeding up and enhancing the convergence of interactive evolution. This work generates spaceship hulls and their weapon and thruster topologies in order to match a user's visual taste as well as conform to a number of constraints aimed for playability and game balance . The 2D shapes representing the spaceship hulls are encoded as pattern-producing networks (CPPNs) and evolved in two populations using the feasible-infeasible 2-population approach (FI-2pop) . One population contains spaceships which fail ad-hoc constraints pertaining to rendering, physics simulation and game balance, and individuals in this population are optimized towards minimizing their distance to feasibility. Removing such spaceships from the population shown to the user reduces the chances of unwanted content and reduces user fatigue.
The second population contains feasible spaceships, which are optimized according to ten fitness dimensions pertaining to common attributes of visual taste such as symmetry, weight distribution, simplicity and size. These fitness dimensions are aggregated into a weighted sum which is used as the feasible population's fitness function. The weights in this quality approximation are adjusted according to a user's selection among a set of presented spaceships . This adaptive aesthetic model aims to enhance the visual patterns behind the user's selection and minimize visual patterns of unselected content, thus generating a completely new set of spaceships which more accurately match the user's tastes. A small number of user selections allows the system to recognize the users' preference, reducing fatigue.
The proposed two-step adaptation system, where (1) the user implicitly adjusts their preference model through content selection and (2) the preference model affects the patterns of generated content, should demonstrate the potential of a flexible tool both for personalizing game content to an end-user's visual taste but also for inspiring a designer's creative task with content guaranteed to be playable, novel and yet conforming to the intended visual style.
Mixed-initiative systems are systems where both humans and computers can "take the initiative," and both contribute to the creative process. The degree to which each party takes the initiative and contributes varies between different systems. At one end of this scale is computer-aided design (CAD), where the human directs the creative process and the computer performs tasks when asked to and according to the specifications of the user. At the other end is interactive evolution, where the computer proposes new artifacts and the user is purely reactive, providing feedback on the computer's suggestions. Both of these approaches have a rich history in games: computer-aided design in many game design tools that include elements of content generation, and interactive evolution in games such as Galactic Arms Race. "True" mixed-initiative interaction, or at least the idea of such systems, has a long history within human-computer interaction and artificial intelligence. Within game content generation, there are so far just a few attempts to realize this vision. Tanagra is a platformer level-generation system that uses constraint satisfaction to complete levels sketched by humans, and regenerates parts of levels to ensure playability as the levels are edited by humans. Sentient Sketchbook assists humans in designing strategy game levels, providing feedback on a number of quality metrics and autonomously suggesting modifications of levels. Ropossum is a level editor for Cut the Rope, which can test the playability of levels and automatically regenerate parts of levels to ensure playability as the level is being edited.
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This article comprises Chapter 11 of the book "Procedural Content Generation in Games: A Textbook and an Overview of Current Research" (edited by Noor Shaker, Julian Togelius and Mark J. Nelson). Information about the book, along with guidelines for citing it, can be found here. The text and images of the article have been adapted for online viewing; a few corrections have also been made.