Explicit representations of problem-solving strategies to support knowledge acquisition

  • Authors:

  • Yolanda Gil;Eric Melz

  • Affiliations:

  • USC/Information Sciences Institute, Marina del Rey, CA;USC/Information Sciences Institute, Marina del Rey, CA

  • Venue:
  • AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
  • Year:
  • 1996

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Abstract

Role-limiting approaches support knowledge acquisition (KA) by centering knowledge base construction on common types of tasks or domain-independent problem-solving strategies. Within a particular problem-solving strategy, domain-dependent knowledge plays specific roles. A KA tool then helps a user to fill these roles. Although role-limiting approaches are useful for guiding KA, they are limited because they only support users in filling knowledge roles that have been built in by the designers of the KA system. EXPECT takes a different approach to KA by representing problem-solving knowledge explicitly, and deriving from the current knowledge base the knowledge gaps that must be resolved by the user during KA. This paper contrasts role-limiting approaches and EXPECT's approach, using the propose-and-revise strategy as an example. EXPECT not only supports users in filling knowledge roles, but also provides support in making other modifications to the knowledge base, including adapting the problem-solving strategy. EXPECT's guidance changes as the knowledge base changes, providing a more flexible approach to knowledge acquisition. This work provides evidence supporting the need for explicit representations in building knowledge-based systems.