Learning Hierarchical Performance Knowledge by Observation

@inproceedings{Lent1999LearningHP,
  title={Learning Hierarchical Performance Knowledge by Observation},
  author={Michael van Lent and John E. Laird},
  booktitle={ICML},
  year={1999}
}
Developing automated agents that intelligently perform complex real world tasks is time consuming and expensive. The most expensive part of developing these intelligent task performance agents involves extracting knowledge from human experts and encoding it into a form useable by automated agents. Machine learning from a sufficiently rich and focused knowledge source can significantly reduce the cost of developing intelligent performance agents by automating the knowledge acquisition and… CONTINUE READING
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