Towards pattern-based mixed-initiative dungeon generation

Baldwin, Alexander; Dahlskog, Steve; Font, José; Holmberg, Johan

doi:10.1145/3102071.3110572

Cited by 33 publications

(42 citation statements)

References 19 publications

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“…EDD uses a single-objective fitness function with a FI2Pop genetic algorithm where fitness is a weighted sum divided equally between (1) the inventorial aspect of the rooms, which relates to the placement of enemies and treasures in relation to doors and target ratios, and (2) the spatial distribution of the design patterns, which relates to the distribution between corridors and rooms, and the meso-patterns that those encompass. An in-depth explanation of EDD's fitness function can be found in [21], [22].…”

Section: Interactive Constrained Map-elitesmentioning

confidence: 99%

Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites

Alvarez

Dahlskog

Font

et al. 2019

2019 IEEE Conference on Games (CoG)

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We propose the use of quality-diversity algorithms for mixed-initiative game content generation. This idea is implemented as a new feature of the Evolutionary Dungeon Designer, a system for mixed-initiative design of the type of levels you typically find in computer role playing games. The feature uses the MAP-Elites algorithm, an illumination algorithm which divides the population into a number of cells depending on their values along several behavioral dimensions. Users can flexibly and dynamically choose relevant dimensions of variation, and incorporate suggestions produced by the algorithm in their map designs. At the same time, any modifications performed by the human feed back into MAP-Elites, and are used to generate further suggestions.

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Section: Interactive Constrained Map-elitesmentioning

confidence: 99%

Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites

Alvarez

Dahlskog

Font

et al. 2019

2019 IEEE Conference on Games (CoG)

Self Cite

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“…Both options fluently alternate during the creation process by means of a workflow of mutual inspiration, through which all manual editions performed by the user are fed into the underlying continuous Evolutionary Algorithm, accommodating them into the procedural suggestions. A detailed description of EDD and its features can be found in [2,3,4,5].…”

Section: The Evolutionary Dungeon Designermentioning

confidence: 99%

“…Subsequent user studies [3,5] carried out with game designers on EDD raised the following areas of improvement: (1) the designers struggled with EDDs capability of understanding the designers intentions and preserving custom designs;…”

Section: The Evolutionary Dungeon Designermentioning

confidence: 99%

Learning the Designer’s Preferences to Drive Evolution

Alvarez

Font

2020

Applications of Evolutionary Computation

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This paper presents the Designer Preference Model, a datadriven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.

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“…It also applies heuristics based on game design patterns to assist users in the creation of dungeon maps for adventure games. The tool follows an interaction design approach with every new verision based on previous users' feedback [40], [41]. All of theses examples show that it is possible to use recommender system techniques to assist human tasks.…”

Section: G Suggestion Engines For Design Assistancementioning

confidence: 99%

Pitako - Recommending Game Design Elements in Cicero

Machado

Gopstein

Nealen

et al. 2019

2019 IEEE Conference on Games (CoG)

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Recommender Systems are widely and successfully applied in e-commerce. Could they be used for design? In this paper, we introduce Pitako 1 , a tool that applies the Recommender System concept to assist humans in creative tasks. More specifically, Pitako provides suggestions by taking games designed by humans as inputs, and recommends mechanics and dynamics as outputs. Pitako is implemented as a new system within the mixedinitiative AI-based Game Design Assistant, Cicero. This paper discusses the motivation behind the implementation of Pitako as well as its technical details and presents usage examples. We believe that Pitako can influence the use of recommender systems to help humans in their daily tasks.

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Towards pattern-based mixed-initiative dungeon generation

Cited by 33 publications

References 19 publications

Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites

Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites

Learning the Designer’s Preferences to Drive Evolution

Pitako - Recommending Game Design Elements in Cicero

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