TR2002-04
Learning Hierarchical Task Models by Demonstration
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- "Learning Hierarchical Task Models by Demonstration", Tech. Rep. TR2002-04, Mitsubishi Electric Research Laboratories, Cambridge, MA, January 2002.BibTeX TR2002-04 PDF
- @techreport{MERL_TR2002-04,
- author = {Andrew Garland and Neal Lesh},
- title = {Learning Hierarchical Task Models by Demonstration},
- institution = {MERL - Mitsubishi Electric Research Laboratories},
- address = {Cambridge, MA 02139},
- number = {TR2002-04},
- month = jan,
- year = 2002,
- url = {https://www.merl.com/publications/TR2002-04/}
- }
,
- "Learning Hierarchical Task Models by Demonstration", Tech. Rep. TR2002-04, Mitsubishi Electric Research Laboratories, Cambridge, MA, January 2002.
Abstract:
Acquiring a domain-specific 'task model' is an essential and notoriously challenging aspect of building knowledge-based systems. This paper presents machine learning techniques which are built into an interface that eases this knowledge acquisition task. These techniques infer hierarchical models, including parameters for non-primitive actions, from partially-annotated demonstrations. Such task models can be used for plan recognition, intelligent tutoring, and other collaborative activities. Among the contributions of this work are a sound and complete learning algorithm and empirical results that measure the utility of possible annotations.