Graph grammar-based controllable generation of puzzles for a learning game about parallel programming

Valls-Vargas, Josep; Zhu, Jichen; Ontañón, Santiago

doi:10.1145/3102071.3102079

Cited by 33 publications

(21 citation statements)

References 24 publications

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“…A puzzle type for which PCG has only recently been applied is programming puzzles. Dong and Barnes developed a template-based puzzle generator for an educational programming game similar to LightBot [61], and Valls-Vargas et al created a constructive generator for parallel programming puzzles based on graph grammars [62].…”

Section: Discussionmentioning

confidence: 99%

Procedural Puzzle Generation: A Survey

Kegel

Haahr

2020

IEEE Trans. Games

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Procedural Content Generation (PCG) for games has existed since the 1980s and is becoming increasingly important for creating gameworlds, backstory and characters across many genres, in particular open-world games such as Minecraft (2011) and No Man's Sky (2016). A particular challenge faced by such games is that the content and/or gameplay may become repetitive. Puzzles constitute an effective technique for improving gameplay by offering players interesting problems to solve, but the use of PCG for generating puzzles has been limited compared with its use for other game elements, and efforts have focused mainly on games that are strictly puzzle games, rather than creating puzzles to be incorporated into other genres. Nevertheless, a significant body of work exists, which allows puzzles of different types to be generated algorithmically, and there is scope for much more research into this area. This paper presents a detailed survey of existing work in PCG for puzzles, reviewing 32 methods within eleven categories of puzzles. For the purpose of analysis, the paper identifies a total of seven salient characteristics related to the methods, which are used to show commonalities and differences between techniques and to chart promising areas for future research.

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Section: Discussionmentioning

confidence: 99%

Procedural Puzzle Generation: A Survey

Kegel

Haahr

2020

IEEE Trans. Games

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“…PCG have has applied in EG approaching a varied set of subjects, such as math [Rodrigues et al 2017, Smith et al 2012, Butler et al 2015, Lara et al 2018, computing [Horn et al 2016, Dong and Barnes 2017, Valls-Vargas et al 2017, Dezani et al 2017, resource management [Aslam et al 2017, Luo et al 2017, Grappiolo et al 2011, evolution [Soule et al 2017], therapy [Duval et al 2017], and reading [Hooshyar et al 2018]. In those studies, PCG was mostly used to generate levels or its components (e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Furthermore, most of those research conducted user studies with respect to their interaction with the game. The exceptions are the cases where the algorithms were analyzed [Soule et al 2017, Smith et al 2012, Valls-Vargas et al 2017, Dong and Barnes 2017 or no user evaluation was performed [Dezani et al 2017]. In contrast, works reporting user-based studies mainly evaluated users' behavior [Butler et al 2015], affective states' changes [Lara et al 2018], learning gains [Hooshyar et al 2018, Rodrigues et al 2017, Horn et al 2016, in-game performance [Aslam et al 2017], opinions [Duval et al 2017], and the proposed approach's performance [Luo et al 2017, Grappiolo et al 2011].…”

Section: Literature Reviewmentioning

confidence: 99%

Playing an Educational Game Featuring Procedural Content Generation: Which Attributes Impact Players’ Curiosity?

Rodrigues

Brancher

2019

RENOTE

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Understanding which and how attributes impact player experience can contribute to designing more tailored tools, providing concerns on how to improve these. However, there is a gap in the understanding of what impacts learners’ experience when interacting with Educational Games (EG) featuring Procedural Content generation (PCG) as these have been scantly used together. This article presents an empirical study on which attributes impact both math’s and game’s curiosity of players when interacting with an EG that uses PCG. The results show the attributes that led to higher or lower curiosity, as well aswhich of them are associated with it. Hence, advancing the understanding of what drives players’ curiosity, contributing to the design of EG that feature PCG.

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“…PCG is a promising idea for experience management since it would allow the EM to adapt the game/experience by generating new content. This idea has been explored in the context of both story generation [63], and level generation for educational games [64].…”

Section: Em-driven Procedural Content Generationmentioning

confidence: 99%

Experience Management in Multi-player Games

Zhu

Ontañón

2019

2019 IEEE Conference on Games (CoG)

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Experience Management studies AI systems that automatically adapt interactive experiences such as games to tailor to specific players and to fulfill design goals. Although it has been explored for several decades, existing work in experience management has mostly focused on single-player experiences. This paper is a first attempt at identifying the main challenges to expand EM to multi-player/multi-user games or experiences. We also make connections to related areas where solutions for similar problems have been proposed (especially group recommender systems) and discusses the potential impact and applications of multi-player EM.

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Graph grammar-based controllable generation of puzzles for a learning game about parallel programming

Cited by 33 publications

References 24 publications

Procedural Puzzle Generation: A Survey

Procedural Puzzle Generation: A Survey

Playing an Educational Game Featuring Procedural Content Generation: Which Attributes Impact Players’ Curiosity?

Experience Management in Multi-player Games

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