RED: A CLASSIFICATORY AND ABDUCTIVE EXPERT SYSTEM (KNOWLEDGE-BASED SYSTEMS, ENGINEERING, ANTIBODY IDENTIFICATION, MEDICAL DIAGNOSIS, AUTOMATED PROBLEM-SOLVING)

This dissertation addresses the development and refinement of knowledge engineering guidelines for the use of certain generic tasks in knowledge-based systems. Generic tasks are a characterization of problem-solving based on generic types of input/output, organizations of knowledge, and control of knowledge evocation.;The application of these guidelines is first illustrated by using them to describe the MDX1 system, a knowledge-based system for the diagnosis of liver disease. MDX1 performs sub-hypothesis selection using the generic task of classification. MDX1's inferencing database embodies the generic task of knowledge-directed information passing. This task provides appropriate data or data abstractions about the problem description.;The use of the guidelines are then illustrated by the design and implementation of the RED/ID subsystem. RED/ID is a prototype subsystem of the RED knowledge-based system designed to perform the interpretations of antigrams for the purpose of antibody identification. RED/ID uses in addition to the MDX1 tasks the abductive assembly/criticism of explanatory hypotheses task. This task assembles the sub-hypotheses returned by sub-hypotheses selection into the overall best available conclusion for the case.;Lessons learned from using the proposed architecture and guildelines for RED/ID are related to knowledge allocation considerations for other knowledge-based systems embodying the tasks in the proposed abduction architecture. A discussion in this regard demonstrates that MYCIN and INTERNIST-1 can be described from the perspective of the proposed abductive architecture and the previously discussed tasks.;The knowledge engineering issues are illustrated by using examples from medical knowledge-based systems within the context of a proposed architecture for abduction. Through these systems, extensions to the guidelines for the epistemic analysis of a domain with the proposed architecture for abduction in mind are discussed. These guidelines are useful for identifying: (1) The nature of the problem-solving types to be undertaken by the knowledge-based system within the architecture; (2) And the portions of the knowledge that need to be allocated to the different portions of the architecture.