Knowledge acquisition for goal prediction in a multi-user adventure game
We present an approach to goal recognition which uses a Dynamic Belief Network to represent domain features needed to identify users' goals and plans. Different network structures have been developed, and their conditional probability distributions have been automatically acquired from training data. These networks show a high degree of accuracy in predicting users' goals. Our approach allows the use of incomplete, sparse and noisy data during both training and testing. We then apply simple learning techniques to learn significant actions in the domain. This speeds up the performance of the most promising dynamic belief networks without loss in predictive accuracy.
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