AAAI Publications, Twenty-Sixth AAAI Conference on Artificial Intelligence

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Goal Recognition with Markov Logic Networks for Player-Adaptive Games
Eun Y. Ha, Jonathan P. Rowe, Bradford W. Mott, James C. Lester

Last modified: 2012-07-14

Abstract


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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