SASHA: the automatic generation of rule-based diagnostic expert systems

ABSTRACT

SASHA is a model-driven application shell which generates expert systems for fault isolation and repair in complex electronic systems. SASHA incorporates an expert system shell and a knowledge acquisition tool into a unique mechanism that elicits data directly from the domain expert and outputs a rule-based diagnostic expert system. SASHA employs causal-model and rule-based representations. It uses a causal model to identify the structure, behavior and relation of a domain object. It uses rules to direct the diagnostic problem solver. SASHA's initial knowledge base consists of a domain independent generalized model of the system under analysis. This underlying model drives knowledge acquisition and the consequent generation of an expert system. SASHA introduces a new concept of Pattern Rules, which are a generalized skeleton of the diagnostic problem solver. During diagnosis, when applied to the domain specific Structural Knowledge base, Pattern Rules dynamically instantiate the actual domain rules. Based on the Pattern Rules notion, three types of rule base organization are proposed and analyzed: Fully Patternized Organization (FPO), Partly Patternized Organization (PPO) and Customized Domain-specific Organization (CDO).

REFERENCES

• 1: Intellicorp KEE Software Development System, "User's Hanual", 1986
• 2: Kahn, G.S., Kepner, A. and Pepper, J., "TEST: A Model-driven Application Shell", in Proc. National Conference on Artificial Intelligence, Seattle, Washington, pp. 814-818, 1987
• 3: Sanjay Mittal , Daniel G. Bobrow , Johan de Kleer, DARN: Toward a community memory for diagnosis and repair tasks, Expert systems: the user interface, Ablex Publishing Corp., Norwood, NJ, 1987
• 4: Shortliffe, E., "Computer-based Medical Consultation: MYCIN," Elsevier, 1976

INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.1 ADMINISTRATIVE DATA PROCESSING
      Subjects: Law

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.4 Knowledge Representation Formalisms and Methods
          Subjects: Representations (procedural and rule-based)
      I.2.6 Learning
          Subjects: Knowledge acquisition

  J. Computer Applications
  J.7 COMPUTERS IN OTHER SYSTEMS
      Subjects: Command and control

General Terms:
Design, Performance