Knowledge acquisition planning: Using multiple sources of knowledge to answer questions in biomedicine

  • Authors:

  • Lawrence Hunter

  • Affiliations:

  • Lister Hill Center, MS-54 National Library of Medcine, Bethesda, MD 20894, U.S.A.

  • Venue:
  • Mathematical and Computer Modelling: An International Journal
  • Year:
  • 1992

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Abstract

By combining techniques from machine learning and robot planning, knowledge acquisition planning provides a framework for automatically deploying diverse and complex analytical tools to extremely large collections of data. KA planning is the process of automatically combining inferential tools such as induction, search, database lookup and statistical analysis into methods for addressing complex query statements. This process depends both on having a domain model that supports subgoal decomposition and a library of KA actions annotated with the type and form of required input data, expected outcomes of the action, estimates of computational resources needed, and so on. By annotating analytical tools in this way, it is possible to automatically select and deploy them and combine their results, even if they run on incompatible platforms. Knowledge acquisition planning was first implemented in a program that learned about diagnosing lung tumors. A larger project is currently underway in the domain of molecular biology.