well as the amount of effort that steering takes. Control also determines to which extentediting those options and parameters causes sensible output changes,i.e.the intuitive responsiveness of a generator.Proper control assures that a generator creates consistent results (e.g.playable levels), while maintaining both the setCopyright (c) 2013 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing pubs-permissions@ieee.org.This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/TCIAIG.2013.2290371, IEEE Transactions on Computational Intelligence and AI in GamesIEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES4of desired properties and variability. The lack of proper parameters or the lack of understanding of what a parameterexactly does, almost always imply poor control over a PCG method, which may lead to undesirable results or evencatastrophic failures.In early PCG methods (e.g.Perlin noise [10]), the focus was more on how the methods worked andwhatcouldbe generated. More recently, researchers became more concerned abouthowthey can achieve the results they want.Meaningful parameters were introduced to help generate content towards a specific output. As PCG methods grewin complexity, and different PCG methods were combined to form more complex generation processes, more controlwas needed as well. As an example, PCG control has evolved towards more natural interaction between designerand machine, with the use of techniques like declarative modeling [11] or controllable agents [12]. More recently,PCG control has expanded towards an even more natural interaction, including the use of gameplay as a meaningfulcontrol parameter [13].As discussed before, the tight bond between gameplay and game space is an essential aspect of dungeons. Therefore,control over PCG methods for dungeons should preferably facilitate such bond, by steering space generation fromgameplay requirements. This can be done in more (e.g.parameters referring to the difficulty of a level) or less (e.g.parameters referring to topology) explicit ways. Therefore, in this survey, we are specially interested in discussingthis dungeon-specific generation challenge:gameplay-based control, where PCG parameters are related to gameplaydata.III. CELLULARAUTOMATAOne of the methods for procedural dungeon generation is cellular automata. This self-organizing structure consistsof a grid of cells in any finite number of dimensions. Each cell has a reference to a set of cells that make up itsneighborhood, and an initial state at zero time (t = 0). To calculate the state of a cell in the next generation (t + 1),a rule set is applied to the current state of the cell and the neighboring ones. After multiple generations, patternsmay form in the grid, which are greatly dependent on the used rules and cell states. The representational model of acellular automaton is a grid of cells and their states. An example set of allowed states would befwall, pathg, suchthat cells represent either a location where a player can go, or a location where the player cannot go.Johnsonet al.[14] use the self-organization capabilities of cellular automata to generatecavelevels. They definethe neighborhood of a cell as its eight surrounding cells (Moore neighborhood), where its possible states areffloor,rock, wallg. After an initial random cell conversion (floor to rock), the