Human-centered knowledge acquisition: a structural learning theory approach

✍ Scribed by David P. Hale; Shane Sharpe; Dwight A. Haworth

• Publisher: Elsevier Science
• Year: 1996
• Tongue: English
• Weight: 274 KB
• Volume: 45
• Category: Article
• ISSN: 1071-5819
• DOI: 10.1006/ijhc.1996.0058

✦ Synopsis

This paper develops the application of structural learning theory (SLT) to support the knowledge engineer (KE) in the knowledge acquisition process and the development of expert systems . The underlying research focuses on the knowledge to elicit from skilled domain problem solvers , and the structure (i . e . form and type) of this knowledge using SLT to guide elicitation and interpretation . SLT explicitly models both declarative and procedural knowledge , while presuming an innate backward-chaining mechanism .

Guidelines based on SLT allow knowledge engineers to concentrate on the human-centered knowledge of domain specific problem solvers . In fact , the SLT model presumes that skilled problem solvers do not automatically divulge all rules . This human-centered , needs-based approach provides a point of departure from previous knowledge acquisition methods and serves as a distinguishing feature of this knowledge acquisition method . Specifically grounded in SLT , distinct rule types are developed to be extracted from skilled domain problem solvers . Based on these rule types , guidelines are developed to aid the KEs in the acquisition process .