Dungeon Digger: Apprenticeship Learning for Procedural Dungeon Building Agents

被引:3
|
作者
Sheffield, Evan C. [1 ]
Shah, Michael D. [1 ]
机构
[1] Northeastern Univ, Coll Comp & Informat Sci, Boston, MA 02115 USA
关键词
Procedural Content Generation; Machine Learning; Inverse Reinforcement Learning; Apprenticeship Learning;
D O I
10.1145/3270316.3271539
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
One of the challenges of generating video game levels procedurally is capturing what in the design of a specific level makes it fun to play. In this paper, we demonstrate our preliminary work on a system which learns from expertly designed game levels to produce new game levels automatically. We developed a platform for designers to create tile based dungeon levels and a level-generating agent which consumes recordings of design sessions to learn and then create its own levels. We evaluate the output of our agent using metrics gathered from a static analysis and a discount usability study using a digital game prototype that renders the level designs. Our preliminary results suggest that this system is capable of generating content that emulates the style of the human designer and approaches the level of fun of human-designed levels.
引用
收藏
页码:603 / 610
页数:8
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