Andrew Garland
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.
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Index Terms
- Learning hierarchical task models by defining and refining examples
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