Learning hierarchical task models by defining and refining examples

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

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Knowledge acquisition

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.2 Design Tools and Techniques
          Subjects: Decision tables

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.4 Knowledge Representation Formalisms and Methods
          Subjects: Representation languages

General Terms:
Measurement, Performance, Theory

Keywords:
knowledge acquisition, programming by demonstration