Mixed-initiative content creation

Liapis, Antonios; Smith, Gillian; Shaker, Noor

doi:10.1007/978-3-319-42716-4_11

Cited by 50 publications

(49 citation statements)

References 29 publications

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“…In the related fields of computational creativity and procedural content generation (mainly for games), we see similar problems, which has led to so-called mixed initiative approaches (Liapis, Smith, & Shaker, 2016) where a human designer and a computer program work together, taking turns, in order to reach a specific design goal. Without human interaction, the available methods would not be able to produce results in a human compatible style.…”

Section: Riedl (2016) Gives An Overview and Vision Of How Ai Can Apprmentioning

confidence: 99%

Social Bots: Human-Like by Means of Human Control?

et al. 2017

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Social bots are currently regarded an influential but also somewhat mysterious factor in public discourse and opinion making. They are considered to be capable of massively distributing propaganda in social and online media, and their application is even suspected to be partly responsible for recent election results. Astonishingly, the term social bot is not well defined and different scientific disciplines use divergent definitions. This work starts with a balanced definition attempt, before providing an overview of how social bots actually work (taking the example of Twitter) and what their current technical limitations are. Despite recent research progress in Deep Learning and Big Data, there are many activities bots cannot handle well. We then discuss how bot capabilities can be extended and controlled by integrating humans into the process and reason that this is currently the most promising way to realize meaningful interactions with other humans. This finally leads to the conclusion that hybridization is a challenge for current detection mechanisms and has to be handled with more sophisticated approaches to identify political propaganda distributed with social bots.

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Section: Riedl (2016) Gives An Overview and Vision Of How Ai Can Apprmentioning

confidence: 99%

Social Bots: Human-Like by Means of Human Control?

et al. 2017

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“…Mixed-initiative content generation tools [3] are designed around the idea of the artist having a conversation with the tool about one specific design across many alterations, with the goal of creating one high-quality design. We propose to adapt this conversational teaching model for application in PCGML systems.…”

Section: Introductionmentioning

confidence: 99%

Addressing the fundamental tension of PCGML with discriminative learning

Karth

Smith

2019

Proceedings of the 14th International Conference on the Foundations of Digital Games

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Procedural content generation via machine learning (PCGML) is typically framed as the task of fitting a generative model to full-scale examples of a desired content distribution. This approach presents a fundamental tension: the more design effort expended to produce detailed training examples for shaping a generator, the lower the return on investment from applying PCGML in the first place. In response, we propose the use of discriminative models (which capture the validity of a design rather the distribution of the content) trained on positive and negative examples. Through a modest modification of WaveFunctionCollapse, a commercially-adopted PCG approach that we characterize as using elementary machine learning, we demonstrate a new mode of control for learning-based generators. We demonstrate how an artist might craft a focused set of additional positive and negative examples by critique of the generator's previous outputs. This interaction mode bridges PCGML with mixed-initiative design assistance tools by working with a machine to define a space of valid designs rather than just one new design.

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“…These digital tools can be thought of as equivalent to a more traditional mixed-initiative PCG system. In a mixed-initiative PCG system, sometimes called co-creative design, a human designer works together with a digital PCG tool [35]. In this section, we give a number of examples of how these digital aides have been integrated into TTRPGs, though we expect there is a great deal more work to be done in this space.…”

Section: Digital Mixed-initiative Agentsmentioning

confidence: 99%

“…We expect that this could lead to a broader understanding of PCG. • Because human players work with analog generation to produce stories in TTRPGs we can consider them parts of analog mixed-initiative generators [35]. This is valuable as such mixed-initiative generators with human and AI partners are still under-researched.…”

Section: Introductionmentioning

confidence: 99%

Tabletop Roleplaying Games as Procedural Content Generators

Guzdial

Acharya

Kreminski

et al. 2020

International Conference on the Foundations of Digital Games

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Tabletop roleplaying games (TTRPGs) and procedural content generators can both be understood as systems of rules for producing content. In this paper, we argue that TTRPG design can usefully be viewed as procedural content generator design. We present several case studies linking key concepts from PCG research-including possibility spaces, expressive range analysis, and generative pipelines-to key concepts in TTRPG design. We then discuss the implications of these relationships and suggest directions for future work uniting research in TTRPGs and PCG. CCS CONCEPTS • Applied computing → Computer games; Media arts; • Computing methodologies → Artificial intelligence.

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Mixed-initiative content creation

Cited by 50 publications

References 29 publications

Social Bots: Human-Like by Means of Human Control?

Social Bots: Human-Like by Means of Human Control?

Addressing the fundamental tension of PCGML with discriminative learning

Tabletop Roleplaying Games as Procedural Content Generators

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