Machine playtesting tools and game moment search engines require exposure to
the diversity of a game's state space if they are to report on or index the
most interesting moments of possible play. Meanwhile, mobile app distribution
services would like to quickly determine if a freshly-uploaded game is fit to
be published. Having access to a semantic map of reachable states in the game
would enable efficient inference in these applications. However, human gameplay
data is expensive to acquire relative to the coverage of a game that it
provides. We show that off-the-shelf automatic exploration strategies can
explore with an effectiveness comparable to human gameplay on the same
timescale. We contribute generic methods for quantifying exploration quality as
a function of time and demonstrate our metric on several elementary techniques
and human players on a collection of commercial games sampled from multiple
game platforms (from Atari 2600 to Nintendo 64). Emphasizing the diversity of
states reached and the semantic map extracted, this work makes productive
contrast with the focus on finding a behavior policy or optimizing game score
used in most automatic game playing research.

研究表明，现成的自动勘探策略可以与人类游戏玩家在相同时间尺度上实现相同的效果，为探索游戏状态空间和提取语义地图等应用贡献了通用方法。