Type of Research: Academic (George Mason University).

Summary:
This is extremely short paper; however their approach and the subject matter (COA analysis) is interesting. “The domain expert is given a specific problem to solve (such as, to “Assess COA411 with respect to the Principle of Objective”) and solves it through task reduction, as illustrated in Figure 1. To perform this assessment, the expert needs a certain amount of information about COA411. This information is obtained through a series of questions and answers that help reduce the initial assessment task to simpler and better defined ones, until the expert has enough information to perform the assessment.

“There are several important results of this modeling process:

1) Necessary concepts and features are identified - they guide the import of relevant ontological knowledge from external repositories such as CYC (Lenat, 1995), Loom (MacGregor, 1999) or Ontolingua (Farquhar et al. 1996), leading to the definition of the agent’s ontology.

2) Each task reduction step represents an example from which the Disciple agent will learn a general rule through the application of a mixed-initiative multistrategy learning method. In particular, the question and the answer from the example reduction guides the agent in generating an explanation of the reduction, which is a central element in rule learning.

3) The learned rules will include generalizations of the natural language phrases from the modeling tree. These phrases are used to generate solutions and justification in natural language. For instance, an abstract justification of an assessment task is generated by simply instantiating the sequence of the questions and answers that led to the assessment.”

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