A Task-Specific Problem-Solving Architecture for Candidate Evaluation

Michel Mitri


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.

Full Text:


DOI: https://doi.org/10.1609/aimag.v12i3.906

Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.