Knowledge Acquisition for Configuration Tasks: The EXPECT Approach

Surya Ramachandran, Yolanda Gil

Configuration systems often use large and complex knowledge bases that need to be maintained and extended over time. The explicit representation of problem-solving knowledge and factual knowledge can greatly enhance the role of a knowledge acquisition tool by deriving from the current knowledge base, the knowledge gaps that must resolved. This paper details EXPECT’s approach knowledge acquisition in the configuration domain using the propose-and-revise strategy as an example. EXPECT supports users in a variety of KA tasks like filling knowledge roles, making modifications to the knowledge base including entering new components, classes and even adapting problem-solving strategies for new tasks. EXPECT’s guidance changes as the knowledge base changes, providing a more flexible approach to knowledge acquisition. The paper also examines the possible use EXPECT as a KA tool in the complex and real world domain of computer configuration.


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