Mechanics automatically recognized via interactive observation

Summerville, Adam; Osborn, Joseph C.; Holmgård, Christoffer; Zhang, Daniel W.

doi:10.1145/3102071.3102104

Cited by 15 publications

(23 citation statements)

References 5 publications

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“…This game combines elements from Plants vs. Zombies (PopCap Games) and R-Type (Irem, 1991) took inspiration from previous works which found similarities in games. These similarities were sometimes related to a whole game genre, like puzzles that have the same mechanics as Tetris [8] or a single mechanic feature like jumps in 2D platform games [9]. We developed our system on top of the VGDL and GVGAI framework to be general across game genres and mechanics.…”

Section: Discussionmentioning

confidence: 99%

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Pitako - Recommending Game Design Elements in Cicero

Machado

Gopstein

Nealen

et al. 2019

2019 IEEE Conference on Games (CoG)

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Recommender Systems are widely and successfully applied in e-commerce. Could they be used for design? In this paper, we introduce Pitako 1 , a tool that applies the Recommender System concept to assist humans in creative tasks. More specifically, Pitako provides suggestions by taking games designed by humans as inputs, and recommends mechanics and dynamics as outputs. Pitako is implemented as a new system within the mixedinitiative AI-based Game Design Assistant, Cicero. This paper discusses the motivation behind the implementation of Pitako as well as its technical details and presents usage examples. We believe that Pitako can influence the use of recommender systems to help humans in their daily tasks.

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Section: Discussionmentioning

confidence: 99%

“…al. [9] analyzes the jump mechanic available in 2D platform games developed for the Nintendo Entertainment System (NES). The work presents commonalities and trends in jumping across games, developers, and game franchise.…”

Section: Introductionmentioning

confidence: 99%

Pitako - Recommending Game Design Elements in Cicero

Machado

Gopstein

Nealen

et al. 2019

2019 IEEE Conference on Games (CoG)

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“…Because some important level objects are sprites and not tiles, we also hope to learn the initial placements of dynamic game objects in the larger map. Mappy identi es abstract game objects by observing hardware sprites over time using the sprite tracker described in [15].…”

Section: Nes Pragmaticsmentioning

confidence: 99%

Automatic mapping of NES games with mappy

Osborn

Summerville

Mateas

2017

Proceedings of the 12th International Conference on the Foundations of Digital Games

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Game maps are useful for human players, general-game-playing agents, and data-driven procedural content generation. ese maps are generally made by hand-assembling manually-created screenshots of game levels. Besides being tedious and error-prone, this approach requires additional e ort for each new game and level to be mapped. e results can still be hard for humans or computational systems to make use of, privileging visual appearance over semantic information. We describe a so ware system, Mappy, that produces a good approximation of a linked map of rooms given a Nintendo Entertainment System game program and a sequence of bu on inputs exploring its world. In addition to visual maps, Mappy outputs grids of tiles (and how they change over time), positions of non-tile objects, clusters of similar rooms that might in fact be the same room, and a set of links between these rooms. We believe this is a necessary step towards developing larger corpora of high-quality semantically-annotated maps for PCG via machine learning and other applications.

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“…Fasterholdt et al [58] studied a number of platformer games to form a general model of jumping, but their (labor-intensive) analysis was limited to four games. Our work building on this [53] automatically performed in-game experiments, allowing us to capture and juxtapose the jumping dynamics of 48 games, a 12-fold increase. Large data-based game studies [59], [60] generally rely on textual data such as Wikipedia or GameFAQs.…”

Section: A Quantitative Game Studiesmentioning

confidence: 99%

Automated game design learning

Osborn

Summerville

Mateas

2017

2017 IEEE Conference on Computational Intelligence and Games (CIG)

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While general game playing is an active field of research, the learning of game design has tended to be either a secondary goal of such research or it has been solely the domain of humans. We propose a field of research, Automated Game Design Learning (AGDL), with the direct purpose of learning game designs directly through interaction with games in the mode that most people experience games: via play. We detail existing work that touches the edges of this field, describe current successful projects in AGDL and the theoretical foundations that enable them, point to promising applications enabled by AGDL, and discuss next steps for this exciting area of study. The key moves of AGDL are to use game programs as the ultimate source of truth about their own design, and to make these design properties available to other systems and avenues of inquiry.

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Mechanics automatically recognized via interactive observation

Cited by 15 publications

References 5 publications

Pitako - Recommending Game Design Elements in Cicero

Pitako - Recommending Game Design Elements in Cicero

Automatic mapping of NES games with mappy

Automated game design learning

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