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AAAI
2012

Goal Recognition with Markov Logic Networks for Player-Adaptive Games

12 years 2 months ago
Goal Recognition with Markov Logic Networks for Player-Adaptive Games
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.
Eun Y. Ha, Jonathan P. Rowe, Bradford W. Mott, Jam
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where AAAI
Authors Eun Y. Ha, Jonathan P. Rowe, Bradford W. Mott, James C. Lester
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