Learning hierarchical task models by defining and refining examples

@inproceedings{Garland2001LearningHT,
  title={Learning hierarchical task models by defining and refining examples},
  author={Andrew Garland and Kathy Ryall and Charles Rich},
  booktitle={K-CAP},
  year={2001}
}
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… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 45 CITATIONS

Automatic task model generation for Interface Agent development

  • Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial
  • 2005
VIEW 4 EXCERPTS
CITES RESULTS, BACKGROUND & METHODS
HIGHLY INFLUENCED

Eliciting User Interface Requirements and Deriving Usability Problems from Scenario Textual Descriptions

  • Enterprise Modelling and Information Systems Architectures
  • 2018
VIEW 3 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

Learning Hierarchical Task Models from Input Traces

  • Computational Intelligence
  • 2016
VIEW 1 EXCERPT
CITES METHODS

Interactive Hierarchical Task Learning from a Single Demonstration

  • 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI)
  • 2015
VIEW 1 EXCERPT
CITES BACKGROUND

PlanBot : A Replanning Agent for Starcraft

Michael Leece, Arnav Jhala
  • 2015

References

Publications referenced by this paper.