Automated game design learning

Abstract

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.

Conceptual Game Expansion

“…Osborn et al [32] proposed automated game design learning, an approach that makes use of emulated games to learn a representation of the game's structure [33] and rules [34]. This approach is most similar to our work in terms of deriving a complete model of game structure and rules.…”

Automated Game Design via Conceptual Expansion

“…In this paper we focus on machine-learned game representations. Osborn et al (2017b) propose automated game design learning, an approach that makes use of emulated games to learn a representation of the game's structure (Osborn, Summerville, and Mateas 2017a) and rules (Summerville, Osborn, and Mateas 2017). This approach is most similar to our own in terms of deriving a complete model of game structure and rules.…”