| 1. |
- Alvarez, Alberto, 1992-, et al.
(författare)
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Assessing the Effects of Interacting with MAP-Elites
- 2021
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Ingår i: Proceedings of the seventeenth {AAAI} Conference on Artificial Intelligence and Interactive Digital Entertainment. - : Association for the Advancement of Artificial Intelligence. - 9781577358718 ; , s. 124-131
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Konferensbidrag (refereegranskat)abstract
- MAP-Elites has been successfully applied to the generation of game content and robot behaviors. However, its behavior and performance when interacted with in co-creative systems is underexplored. This paper analyzes the implications of synthetic interaction for the stability and adaptability of MAP-Elites in such scenarios. We use pre-recorded human-made level design sessions with the Interactive Constrained MAP-Elites (IC MAP-Elites). To analyze the effect of each edition step in the search space over time using different feature dimensions, we introduce Temporal Expressive Range Analysis (TERA). With TERAs, MAP-Elites is assessed in terms of its adaptability and stability to generate diverse and high-performing individuals. Our results show that interactivity, in the form of design edits and MAP-Elites adapting towards them, directs the search process to previously unexplored areas of the fitness landscape and points towards how this could improve and enrich the co-creative process with quality-diverse individuals.
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| 2. |
- Alvarez, Alberto, 1992-, et al.
(författare)
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Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites
- 2019
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Konferensbidrag (refereegranskat)abstract
- 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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| 3. |
- Alvarez, Alberto, 1992-
(författare)
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Exploring Game Design through Human-AI Collaboration
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Game design is a hard and multi-faceted task that intertwines different gameplay mechanics, audio, level, graphic, and narrative facets. Games' facets are developed in conjunction with others with a common goal that makes games coherent and interesting. These combinations result in plenty of games in diverse genres, which usually require a collaboration of a diverse group of designers. Collaborators can take different roles and support each other with their strengths resulting in games with unique characteristics. The multi-faceted nature of games and their collaborative properties and requirements make it an exciting task to use Artificial Intelligence (AI). The generation of these facets together requires a holistic approach, which is one of the most challenging tasks within computational creativity. Given the collaborative aspect of games, this thesis approaches their generation through Human-AI collaboration, specifically using a mixed-initiative co-creative (MI-CC) paradigm. This paradigm creates an interactive and collaborative scenario that leverages AI and human strengths with an alternating and proactive initiative to approach a task. However, this paradigm introduces several challenges, such as Human and AI goal alignment or competing properties.In this thesis, game design and the generation of game facets by themselves and intertwined are explored through Human-AI collaboration. The AI takes a colleague's role with the designer, arising multiple dynamics, challenges, and opportunities. The main hypothesis is that AI can be incorporated into systems as a collaborator, enhancing design tools, fostering human creativity, and reducing workload. The challenges and opportunities that arise from this are explored, discussed, and approached throughout the thesis. As a result, multiple approaches and methods such as quality-diversity algorithms and designer modeling are proposed to generate game facets in tandem with humans, create a better workflow, enhance the interaction, and establish adaptive experiences.
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| 4. |
- Alvarez, Alberto, 1992-
(författare)
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Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- As AI develops, grows, and expands, the more benefits we can have from it. AI is used in multiple fields to assist humans, such as object recognition, self-driving cars, or design tools. However, AI could be used for more than assisting humans in their tasks. It could be employed to collaborate with humans as colleagues in shared tasks, which is usually described as Mixed-Initiative (MI) paradigm. This paradigm creates an interactive scenario that leverage on AI and human strengths with an alternating and proactive initiative to approach a task. However, this paradigm introduces several challenges. For instance, there must be an understanding between humans and AI, where autonomy and initiative become negotiation tokens. In addition, control and expressiveness need to be taken into account to reach some goals. Moreover, although this paradigm has a broader application, it is especially interesting for creative tasks such as games, which are mainly created in collaboration. Creating games and their content is a hard and complex task, since games are content-intensive, multi-faceted, and interacted by external users. Therefore, this thesis explores MI collaboration between human game designers and AI for the co-creation of games, where the AI's role is that of a colleague with the designer. The main hypothesis is that AI can be incorporated in systems as a collaborator, enhancing design tools, fostering human creativity, reducing their workload, and creating adaptive experiences. Furthermore, This collaboration arises several dynamic properties such as control, expressiveness, and initiative, which are all central to this thesis. Quality-Diversity algorithms combined with control mechanisms and interactions for the designer are proposed to investigate this collaboration and properties. Designer and Player modeling is also explored, and several approaches are proposed to create a better workflow, establish adaptive experiences, and enhance the interaction. Through this, it is demonstrated the potential and benefits of these algorithms and models in the MI paradigm.
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| 5. |
- Alvarez, Alberto, 1992-, et al.
(författare)
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Interactive Constrained MAP-Elites : Analysis and Evaluation of the Expressiveness of the Feature Dimensions
- 2022
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Ingår i: IEEE Transactions on Games. - : IEEE. - 2475-1502 .- 2475-1510. ; 14:2, s. 202-211
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Tidskriftsartikel (refereegranskat)abstract
- We propose the Interactive Constrained MAP-Elites, a quality-diversity solution for game content generation, implemented as a new feature of the Evolutionary Dungeon Designer (EDD): a mixed-initiative co-creativity tool for designing dungeons. The feature uses the MAP-Elites algorithm, an illumination algorithm that segregates the population among several cells depending on their scores with respect to different behavioral dimensions. Users can flexibly and dynamically alternate between these dimensions anytime, thus guiding the evolutionary process in an intuitive way, and then incorporate suggestions produced by the algorithm in their room designs. At the same time, any modifications performed by the human user will feed back into MAP-Elites, closing a circular workflow of constant mutual inspiration. This paper presents the algorithm followed by an in-depth evaluation of the expressive range of all possible dimension combinations in several scenarios, and discusses their influence in the fitness landscape and in the overall performance of the procedural content generation in EDD.
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| 6. |
- Alvarez, Alberto, 1992-, et al.
(författare)
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Story Designer: Towards a Mixed-Initiative Tool to Create Narrative Structures
- 2022
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Ingår i: FDG '22: Proceedings of the 17th International Conference on the Foundations of Digital Games. - New York, NY, USA : ACM Digital Library. - 9781450397957
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Konferensbidrag (refereegranskat)abstract
- Narratives are a predominant part of games, and their design poses challenges when identifying, encoding, interpreting, evaluating, and generating them. One way to address this would be to approach narrative design in a more abstract layer, such as narrative structures. This paper presents Story Designer, a mixed-initiative co-creative narrative structure tool built on top of the Evolutionary Dungeon Designer (EDD) that uses tropes, narrative conventions found across many media types, to design these structures. Story Designer uses tropes as building blocks for narrative designers to compose complete narrative structures by interconnecting them in graph structures called narrative graphs. Our mixed-initiative approach lets designers manually create their narrative graphs and feeds an underlying evolutionary algorithm with those, creating quality-diverse suggestions using MAP-Elites. Suggestions are visually represented for designers to compare and evaluate and can then be incorporated into the design for further manual editions. At the same time, we use the levels designed within EDD as constraints for the narrative structure, intertwining both level design and narrative. We evaluate the impact of these constraints and the system’s adaptability and expressiveness, resulting in a potential tool to create narrative structures combining level design aspects with narrative.
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| 7. |
- Alvarez, Alberto, 1992-, et al.
(författare)
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Toward Designer Modeling Through Design Style Clustering
- 2022
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Ingår i: IEEE Transactions on Games. - : IEEE. - 2475-1502 .- 2475-1510. ; 14:4, s. 676-686
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Tidskriftsartikel (refereegranskat)abstract
- We propose modeling designer style in mixed-initiative game content creation tools as archetypical design traces. These design traces are formulated as transitions between design styles; these design styles are in turn found through clustering all intermediate designs along the way to making a complete design. This method is implemented in the Evolutionary Dungeon Designer, a research platform for mixed-initiative systems to create adventure and dungeon crawler games. We present results both in the form of design styles for rooms, which can be analyzed to better understand the kind of rooms designed by users, and in the form of archetypical sequences between these rooms, i.e., Designer Personas.
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| 8. |
- Andersson, Jacob, et al.
(författare)
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Gamification of publication metadata : to playfully explore our data
- 2015
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This report describes a project that aimed to create game concepts and games based on a set of data about research publications. This was done using linked open data, procedural content generation of game content, and iterative game development, with the aim of creating useful games and game concepts.
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| 9. |
- Cardona, Andrew Borg, et al.
(författare)
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Open Trumps, a Data Game
- 2014
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Ingår i: FDG 2014 Proceedings. - : Society for the Advancement of the Science of Digital Games.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- We present Open Trumps, a version of the popular card game Top Trumps with decks that are procedurally generated based on open data. The game is played among multiple players through drawing cards and selecting the feature that is most likely to trump the same feature on the other players’ cards. Players can generate their own decks through choosing a suitable dataset and setting certain attributes; the generator then generates a balanced and playable deck using evolutionary computation. In the example dataset, each card represents a country and the features represent such entities as GDP per capita, mortality rate or tomato production, but in principle any dataset organised as instances with numerical features could be used. We also report the results of an evaluation intended to investigate both player experience and the hypothesis that players learn about the data underlying the deck they play with, since understanding the data is key to playing well. The results show that players enjoy playing the game, are enthusiastic about its potential and answer questions related to decks they have played significantly better than questions related to decks they have not played.
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| 10. |
- Dahlskog, Steve, et al.
(författare)
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A multi-level level generator
- 2014
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Konferensbidrag (refereegranskat)abstract
- Generating content at multiple levels of abstraction simultaneously is an open challenge in procedural content generation. Representing and automatically replicating the style of a human designer is another. This paper addresses both of these challenges through extending a previously devised methodology for pattern-based level generation. This method builds on an analysis of Super Mario Bros levels into three abstraction levels: micro-, meso- and macro-patterns. Micro-patterns are then used as building blocks in a search-based PCG approach that searches for macro-patterns, which are defined as combinations of meso-patterns. Results show that we can successfully generate levels that replicate the macro-patterns of selected input levels, and we argue that this constitutes an approach to automatically analysing and replicating style in level design.
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