TR2001-26

Learning Hierarchical Task Models by Defining and Refining Examples

    •  Garland, A., Ryall, K., Rich, C., "Learning Hierarchical Task Models by Defining and Refining Examples", ACM International Conference on Knowledge Capture (KCAP), October 2001, pp. 44-51.
      BibTeX TR2001-26 PDF
      • @inproceedings{Garland2001oct,
      • author = {Garland, A. and Ryall, K. and Rich, C.},
      • title = {Learning Hierarchical Task Models by Defining and Refining Examples},
      • booktitle = {ACM International Conference on Knowledge Capture (KCAP)},
      • year = 2001,
      • pages = {44--51},
      • month = oct,
      • isbn = {1-58113-380-4},
      • url = {https://www.merl.com/publications/TR2001-26}
      • }
Abstract:

Task models are used in many areas of computer science including planning, intelligent tutoring, plan recognition, interface design, and decision theory. However, developing task models is a significant practical challenge. We present a task model development environment centered around a machine learning engine that infers task models from examples. A novel aspect of the environment is support for a domain expert to refine past examples as he or she develops a clearer understanding of how to model the domain. Collectively, these examples constitute a \"test suite\" that the development environment manages in order to verify that changes to the evolving task model do not have unintended consequences.

 

  • Related News & Events

    •  NEWS    KCAP 2001: publication by Charles Rich and others
      Date: October 21, 2001
      Where: ACM International Conference on Knowledge Capture (KCAP)
      Brief
      • The paper "Learning Hierarchical Task Models by Defining and Refining Examples" by Garland, A., Ryall, K. and Rich, C. was presented at the ACM International Conference on Knowledge Capture (KCAP).
    •