Open AccessA Task‐Specific Problem‐Solving Architecture for Candidate Evaluation
Author(s) -
Mitri Michel
Publication year - 1991
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v12i3.906
Subject(s) - task (project management) , computer science , encode , architecture , table (database) , domain (mathematical analysis) , artificial intelligence , machine learning , systems engineering , data mining , engineering , art , mathematical analysis , biochemistry , chemistry , mathematics , visual arts , gene
Task‐specific architectures are a growing area of expert system research. Evaluation is one task that is required in many problem‐solving domains. This article describes a task‐specific, domain‐independent architecture for candidate evaluation. I discuss the task‐specific architecture approach to knowledge‐based system development. Next, I present a review of candidate evaluation methods that have been used in ai and psychological modeling, focusing on the distinction between discrete truth table approaches and continuous linear models. Finally, I describe a task‐specific expert system shell, which includes a development environment (Ceved) and a run‐time consultation environment (Ceval). This shell enables nonprogramming domain experts to easily encode and represent evaluation‐type knowledge and incorporates the encoded knowledge in performance systems.