Handling Uncertainty during Plan Recognition in Task-Oriented
  Consultation Systems
  During interactions with human consultants, people are used to providing
partial and/or inaccurate information, and still be understood and assisted. We
attempt to emulate this capability of human consultants; in computer
consultation systems. In this paper, we present a mechanism for handling
uncertainty in plan recognition during task-oriented consultations. The
uncertainty arises while choosing an appropriate interpretation of a user?s
statements among many possible interpretations. Our mechanism handles this
uncertainty by using probability theory to assess the probabilities of the
interpretations, and complements this assessment by taking into account the
information content of the interpretations. The information content of an
interpretation is a measure of how well defined an interpretation is in terms
of the actions to be performed on the basis of the interpretation. This measure
is used to guide the inference process towards interpretations with a higher
information content. The information content for an interpretation depends on
the specificity and the strength of the inferences in it, where the strength of
an inference depends on the reliability of the information on which the
inference is based. Our mechanism has been developed for use in task-oriented
consultation systems. The domain that we have chosen for exploration is that of
a travel agency.
