Goal recognition in digital games involves inferring players' goals from observed sequences of low level player actions. Goal recognition models support player adaptive digital games, which dynamically augment game events in response to player choices for a range of applications, including entertainment, training, and education. However, digital games pose significant challenges for goal recognition, such as exploratory actions and ill defined goals. This paper presents a goal recognition framework based on Markov logic networks (MLNs). The model's parameters are directly learned from a corpus that was collected from player interactions with a non linear educational game. An empirical evaluation demonstrates that the MLN goal recognition framework accurately predicts players' goals in a game environment with exploratory actions and ill defined goals.
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