Towards a Taxonomy for the Clarification of PCG Actors' Roles

Craveirinha, Rui; Barreto, Nuno; Roque, Licínio

doi:10.1145/2967934.2968086

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…We expect designers to not anticipate a substantial part of these interactions, making their hand-crafted models imprecise. The same inflexibility applies when manually crafting gameplay metrics to gather human player feedback implicitly [12].…”

Section: The Challenge Of Assessing Px In Pcgmentioning

confidence: 96%

“…HCI's instruments could still be applied when PCG is used as a tool during development (cf. [12]), and generated content is carefully curated by designers for the final game. However, the full power of PCG, especially for replayability, can only be unleashed when PCG is used in the shipped game, either offline before-, or online during play.…”

Section: The Challenge Of Assessing Px In Pcgmentioning

confidence: 99%

“…In other cases, generators are kept simple by relying on small grammars or noise-based algorithms [49]. These constraints make the explicit [12] prediction of player experience obsolete, but they considerably limit the expressive range [47] of generators, and thereby the actual benefits of PCG in practice.…”

Section: The Challenge Of Assessing Px In Pcgmentioning

confidence: 99%

“…Some theory-driven approaches are entirely independent of the player, but rely on designers instead: guided by their intuition and/or some qualitative theory, the designers must manually establish a functional mapping between content or gameplay features and experiential qualities [12]. While Yannakakis and Togelius [56] only consider approaches based on static game content, we explicitly include simulation-based approaches [52] in this category, where gameplay features are extracted from the simulation of human play with AI agents.…”

Section: The Challenge Of Assessing Px In Pcgmentioning

confidence: 99%

“…In this section, we have shown exhaustively (cf. [12]) that there is no means to assess PX in PCG in a sufficiently precise and unobtrusive way without constraining the power of the content generator. In summary, the identified challenge of assessing PX in PCG is twofold: we have to assess PX independently of human players, while not relying on designers' knowledge about a game's semantics.…”

Section: The Challenge Of Assessing Px In Pcgmentioning

confidence: 99%

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Predicting Player Experience without the Player.

Guckelsberger

Salge

Gow

et al. 2017

Proceedings of the Annual Symposium on Computer-Human Interaction in Play

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A key challenge of procedural content generation (PCG) is to evoke a certain player experience (PX), when we have no direct control over the content which gives rise to that experience. We argue that neither the rigorous methods to assess PX in HCI, nor specialised methods in PCG are sufficient, because they rely on a human in the loop. We propose to address this shortcoming by means of computational models of intrinsic motivation and AI game-playing agents. We hypothesise that our approach could be used to automatically predict PX across games and content types without relying on a human player or designer. We conduct an exploratory study in level generation based on empowerment, a specific model of intrinsic motivation. Based on a thematic analysis, we find that empowerment can be used to create levels with qualitatively different PX. We relate the identified experiences to established theories of PX in HCI and game design, and discuss next steps.

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Section: The Challenge Of Assessing Px In Pcgmentioning

confidence: 96%

Section: The Challenge Of Assessing Px In Pcgmentioning

confidence: 99%

Section: The Challenge Of Assessing Px In Pcgmentioning

confidence: 99%

Section: The Challenge Of Assessing Px In Pcgmentioning

confidence: 99%

Section: The Challenge Of Assessing Px In Pcgmentioning

confidence: 99%

See 3 more Smart Citations

Predicting Player Experience without the Player.

Guckelsberger

Salge

Gow

et al. 2017

Proceedings of the Annual Symposium on Computer-Human Interaction in Play

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Recent Advances in Procedural Generation of Buildings: From Diversity to Integration

Kutzias¹,

Mammen²

2024

IEEE Trans. Games

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We survey the state-of-the-art in procedural generation of buildings (PGB). We analysed the design choices, capabilities and limitations of 71 PGB approaches. We structured our analysis based on a taxonomy of procedural content generation (PCG) that we adapted to PGB by adding relevant details. Overall, we identified 13 taxonomical aspects which we organised in three groups: 1) Generative Capabilities, 2) Accessibility Aspects and 3) Technological Aspects of the implementation. It is shown that the generation of different parts of buildings is easiest to accomplish with different PGB techniques. While some approaches built on other ones or even integrate two or more of them to arrive at solutions with a greater effective generative scope, all of them still exhibit rather basic limitations. For instance, the choice of room geometries might be limited to rectangular rooms, they might require numerous manual creation steps, or they might yield potentially invalid output. In addition, only few approaches implement fitting interiors and exteriors which can be used as complete, traversable buildings in virtual environments. Hence, the combination and therefore integration of different approaches for different parts of buildings is a major concern of procedural building generators, rendering their compatibility a characteristic of central importance.

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