Learning relational probabilistic action models for online planning with decision forests
Pages 248 - 253
Abstract
Performance of online planning correlates with predictive quality of the used model. A predictive domain model manually specified at design time may yield poor predictive quality at runtime, either due to specification errors or due to unexpected change. Learning a predictive model from runtime observations allows to identify and recover from predictive inaccuracy due to erroneous specification or unexpected change. We propose Relational Probabilistic Action Forests, an approach for learning probabilistic predictive action models for relational data with decision forests. This enables generalization over discrete relational training data, yielding fast learning rates by exploitation of relational structure. We empirically demonstrate the effectiveness of the approach.
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- Learning relational probabilistic action models for online planning with decision forests
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Published: 04 April 2016
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