MDS: An Integrated Architecture for Associational and Model-Based Diagnosis

This paper discusses the design and implementation of an integrated diagnosis system, MDS (Multi-level Diagnosis System), which combines associational and model-based approaches to diagnosis. The design and implementation of the associational module is tailored to achieving efficiency in routine diagnostic problem solving, and to providing a desirable interface for the users. The model-based diagnosis module is developed to achieve completeness and consistency in the fault isolation task, and to avoid the brittleness that often occurs in associational systems. MDS addresses the important issue of combining the use of “deeper” knowledge in the form of a system model with “shallow” (or associational) knowledge, using a diagnostic controller to improve completeness and consistency without sacrificing efficiency. The diagnostic controller also employs a methodology for automated knowledge refinement by identifying incomplete and inconsistent rules and diagnostic tests in the associational module, and then by performing updates to correct the problems. This paper focuses on the design and implementation of the diagnostic controller.

[1]  Pieter J. Mosterman,et al.  Diagnosis of continuous valued systems in transient operating regions , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[2]  Johan de Kleer.  Focusing on Probable Diagnoses , 1991, AAAI.

[3]  Gautam Biswas,et al.  Combined qualitative-quantitative steady-state diagnosis of continuous-valued systems , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[4]  E. Shortliffe.  Computer-based medical consultations: mycin (elsevier north holland , 1976 .

[5]  Raymond J. Mooney,et al.  Theory Refinement Combining Analytical and Empirical Methods , 1994, Artif. Intell..

[6]  Ramesh S. Patil,et al.  Causal representation of patient illness for electrolyte and acid-base diagnosis , 1981 .

[7]  Chen-Ching Liu,et al.  Rule-Based System Validation through Automatic Identification of Equivalence Classes , 1997, IEEE Trans. Knowl. Data Eng..

[8]  Susan Craw,et al.  Representing Problem-Solving for Knowledge Refinement , 1999, AAAI/IAAI.

[9]  Nada Lavrac,et al.  The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains , 1986, AAAI.

[10]  Randall Davis,et al.  Model-based reasoning: troubleshooting , 1988 .

[11]  B. J. Glass,et al.  TEXSYS: a large scale demonstration of model-based real-time control of a space station subsystem , 1991, [1991] Proceedings. The Seventh IEEE Conference on Artificial Intelligence Application.

[12]  Wolfgang Nejdl,et al.  Diamon: a model-based troubleshooter based on qualitative reasoning , 1993, IEEE Expert.

[13]  Frank J. Pipitone,et al.  Model-Based Probabilistic Reasoning for Electronics Troubleshooting , 1983, IJCAI.

[14]  Eugene C. Freuder,et al.  Diagnosing interoperability problems by enhancing constraint satisfaction with case-based reasoning , 1998 .

[15]  Philippe Dague,et al.  Troubleshooting: When Modeling Is the Trouble , 1987, AAAI.

[16]  Giancarlo Mauri,et al.  Evaluating Performance and Quality of Knowledge-Based Systems: Foundation and Methodology , 1993, IEEE Trans. Knowl. Data Eng..

[17]  Gautam Biswas,et al.  A Formal Modeling Scheme for Continuous Systems: Focus on Diagnosis , 1993, IJCAI.

[18]  Michael J. Pazzani,et al.  Revision of Production System Rule-Bases , 1994, ICML.

[19]  Peter Struss,et al.  "Physical Negation" Integrating Fault Models into the General Diagnostic Engine , 1989, IJCAI.

[20]  Yun Peng,et al.  Plausibility of Diagnostic Hypotheses: The Nature of Simplicity , 1986, AAAI.

[21]  Gautam Biswas,et al.  Using the Dempster-Shafer Scheme in a Mixed-Initiative Expert System Shell , 1987, UAI.

[22]  Pamela K. Fink,et al.  Expert Systems and Diagnostic Expertise in the Mechanical and Electrical Domains , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[23]  Casimir A. Kulikowski,et al.  A Model-Based Consultation System for the Long-Term Management of Glaucoma , 1977, IJCAI.

[24]  Ye Huang.  Using Case-Based Techniques to Enhance Constraint Satisfaction Problem Solving , 1996, Appl. Artif. Intell..

[25]  Giorgio Tornielli,et al.  A Diagnostic Algorithm based on Models at Different Level of Abstraction , 1989, IJCAI.

[26]  Bernard L. Palowitch.  Fault diagnosis of process plants using causal models , 1987 .

[27]  Dean Karnopp,et al.  Introduction to physical system dynamics , 1983 .

[28]  Benjamin Kuipers,et al.  Model-Based Monitoring of Dynamic Systems , 1989, IJCAI.

[29]  Luigi Portinale,et al.  ADAPtER: An Integrated Diagnostic System Combining Case-Based and Abductive Reasoning , 1995, ICCBR.

[30]  Xudong Yu.  Multi-level reasoning and diagnosis for complex continuous-valued systems , 1992 .

[31]  B. Chandrasekaran,et al.  Generic Tasks for Knowledge-Based Reasoning: The "Right" Level of Abstraction for Knowledge Acquisition , 1987, Int. J. Man Mach. Stud..

[32]  Hiroshi Tanaka,et al.  Automated Discovery of Medical Expert System Rules from Clinical Databases Based on Rough Sets , 1996, KDD.

[33]  Pietro Torasso,et al.  A Utility-Based Approach to Learning in a Mixed Case-Base and Model-Based Reasoning Architecture , 1997, ICCBR.

[34]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[35]  E H Shorthffe,et al.  Computer-based medical consultations mycin , 1976 .

[36]  Brian Falkenhainer,et al.  Compositional Modeling: Finding the Right Model for the Job , 1991, Artif. Intell..

[37]  Yolanda Gil,et al.  Designing Scripts to Guide Users in Modifying Knowledge-based Systems , 1999, AAAI/IAAI.

[38]  Brian C. Williams,et al.  Diagnosing Multiple Faults , 1987, Artif. Intell..

[39]  Mohammed H. Sqalli,et al.  Diagnosing interoperability problems and debugging models by enhancing constraint satisfaction with case-based reasoning , 2002 .