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Learning Hierarchical Task Models By Demonstration Content Areas : knowledge acquisition , machine learning
| Content Provider | Semantic Scholar |
|---|---|
| Author | Garland, Andrew Lesh, Neal |
| Copyright Year | 2003 |
| Abstract | Acquiring a domain-specifictask modelis an essential and notoriously challenging aspect of building knowledge-based systems. This paper presents machine learning techniques that ease 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. Submitted to the Eighteenth International Joint Conference on Artificial Intelligence ( IJCAI-03), |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.cs.brandeis.edu/~aeg/papers/garland.tr2003-01.pdf |
| Alternate Webpage(s) | http://www.merl.com/papers/docs/TR2003-01.pdf |
| Alternate Webpage(s) | http://www.merl.com/papers/docs/TR2003-001.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
