Okies: a troubleshooter in the factory

ABSTRACT

OKIES is an expert system that troubleshoots newly assembled AT&T 3B2 computer systems. All AT&T 3B2 models and configurations are analyzed by OKIES. The expert system uses an architecture-based design to apply the same knowledge to different machines. An architectural model of the machine is constructed when the session begins. This model is used to determine which tests are applicable, the components that compose the machine, and how the machine should be fixed. OKIES was built as a production system using the OPS/83[1] language. All inference is done by matching. No search or backtracking is performed. The first OKIES prototype used rules generated by a conceptual clustering system. A diagnosis is interactively developed by first having the user pick among problem descriptions. The expert system then refines this by asking the user questions. For instance, the user is asked to examine hardware connections, run tests and report on error messages. If the expert system still can not classify the problem it requests further tests or presents a new problem classification. After determining the problem a treatment is prescribed. The treatment depends on the problem found, the machine's configuration, and the machine's prior history. The current organization of OKIES is presented along with a description of the process by which it was built. Initially the system was developed ad hoc with structure later imposed. Specifically, generalization and decision trees were used to organize the knowledge.

REFERENCES

• 1: OPS/83 User's Manual, System Version 2.2, Production Systems Technologies, Inc., 642 Gettysburg St., Pittsburgh, PA 15206, 1986.
• 2: Patrick Henry Winston, Artificial intelligence (2nd ed.), Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1984
• 3: James R. Rowland , Gregg T. Vesonder, Incremental conceptual clustering from existing databases, Proceedings of the 15th annual conference on Computer Science, p.80-87, February 1987, St. Louis, Missouri, United States [doi>10.1145/322917.322930]
• 4: Stefik, Mark, Daniel G. Bobrow, Sanjay Mittal and Lynn Conway, "Knowledge Programming in LOOPS: Report on an Experimental Course", AI Magazine, p3, Fall 1983.
• 5: Forgy, C.L. "RETE: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem", Arti)tcial Intelligence, 19, (1982), 17-37.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems
          Subjects: Industrial automation

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

General Terms:
Design, Performance