A Dynamic-Bayesian Network framework for modeling and evaluating learning from observation

作者：

Highlights：

• We present a unified framework for learning from observation (LfO).

• We present a Dynamic Bayesian Network (DBN) model of LfO.

• We present a novel set of evaluation metrics for LfO algorithms.

• We show evidence that our metrics better capture LfO algorithm performance than metrics used in previous LfO work.

摘要

•We present a unified framework for learning from observation (LfO).•We present a Dynamic Bayesian Network (DBN) model of LfO.•We present a novel set of evaluation metrics for LfO algorithms.•We show evidence that our metrics better capture LfO algorithm performance than metrics used in previous LfO work.

论文关键词：Learning from observation,Dynamic Bayesian Networks

论文评审过程：Available online 12 March 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.02.049