Tabletop Roleplaying Games as Procedural Content Generators

Guzdial, Matthew; Acharya, Devi; Kreminski, Max; Cook, Michael; Eladhari, Mirjam Palosaari; Liapis, Antonios; Sullivan, Anne

doi:10.1145/3402942.3409605

Cited by 7 publications

(2 citation statements)

References 25 publications

(21 reference statements)

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“…These 2D plots can then be used to understand the character and diversity present in individual generative spaces, but also those from alternative generators compare to each other. ERA has most commonly been applied to work within its original domain of PCG for game levels, but it has also found a place in alternative areas of PCG research such as visualising the output of poetry generators [15], tabletop game outcomes [10] and emergent narrative systems [16].…”

Section: Expressive Range Analysismentioning

confidence: 99%

“…These terms were popularised by Mike Cook in their work [4] and they help to highlight the strengths and weaknesses of ERA, as well as the potential benefits of this work's approach. Unfortunately the definition of these terms is not universally accepted, and there are high profile works that use generative space and possibility space interchangeably [10]. The distinction remains very conceptually useful though, and we hope the terminology will become more ubiquitous in future.…”

Section: Generative Space Possibility Spaces and Eramentioning

confidence: 99%

See 1 more Smart Citation

The Right Variety: Improving Expressive Range Analysis with Metric Selection Methods

Withington

Tokarchuk

2023

Proceedings of the 18th International Conference on the Foundations of Digital Games

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Expressive Range Analysis (ERA), an approach for visualising the output of Procedural Content Generation (PCG) systems, is widely used within PCG research to evaluate and compare generators, often to make comparative statements about their relative performance in terms of output diversity and search space exploration. Producing a standard ERA visualisation requires the selection of two metrics which can be calculated for all generated artefacts to be visualised. However, to our knowledge there are no methodologies or heuristics for justifying the selection of a specific metric pair over alternatives. Prior work has typically either made a selection based on established but unjustified norms, designer intuition, or has produced multiple visualisations across all possible pairs. This work aims to contribute to this area by identifying valuable characteristics of metric pairings, and by demonstrating that pairings that have these characteristics have an increased probability of producing an informative ERA projection of the underlying generator. We introduce and investigate three quantifiable selection criteria for assessing metric pairs, and demonstrate how these criteria can be operationalized to rank those available. Though this is an early exploration of the concept of quantifying the utility of ERA metric pairs, we argue that the approach explored in this paper can make ERA more useful and usable for both researchers and game designers. INTRODUCTIONProcedural Content Generation (PCG) systems for games can often produce huge amounts of diverse artefacts and experiences. Whether it is the billions of possible maps of Minecraft [18] or the quintillions of planets in No Man Sky [6], the total volume of a PCG system's possible output, commonly referred to as its 'generative space' can be huge and complex. The challenge of understanding and usefully comparing generative spaces is a vital one to both

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Section: Expressive Range Analysismentioning

confidence: 99%

Section: Generative Space Possibility Spaces and Eramentioning

confidence: 99%