D‐KAT: a deep knowledge acquisition tool

: This paper describes a system of shallow and deep knowledge acquisition and representation for diagnostic expert systems. The acquisition system is integrated into a diagnostic expert system shell. Shallow knowledge is represented in a failure model as a set of cause-effect relations among the possible faults, while deep knowledge is represented in three deep models: a functional, a deep causal and a taxonomic model. The acquisition and the representation of all the models are fully integrated. The deep knowledge is used by the final expert system in order to provide the user with deep explanations of the cause-effect relations of the failure model.

[1]  John H. Boose,et al.  Selecting Knowledge Acquisition Tools and Strategies Based on Application Characteristics , 1989, Int. J. Man Mach. Stud..

[2]  Silvano Mussi,et al.  Acquiring and representing strategic knowledge in the diagnosis domain , 1990 .

[3]  Johan de Kleer,et al.  How Circuits Work , 1984, Artif. Intell..

[4]  Donald Michie High-Road and Low-Road Programs , 1981, AI Mag..

[5]  Larry J. Eshelman,et al.  MOLE: A Tenacious Knowledge-Acquisition Tool , 1987, Int. J. Man Mach. Stud..

[6]  Michael Grimes,et al.  KNACK - Report-Driven Knowledge Acquisition , 1987, Int. J. Man Mach. Stud..

[7]  Lawrence M. Fagan,et al.  Use of a Domain Model to Drive an Interactive Knowledge-Editing Tool , 1987, Int. J. Man Mach. Stud..

[8]  Ingo Ruhmann,et al.  KRITON: A Knowledge-Acquisition Tool for Expert Systems , 1987, Int. J. Man Mach. Stud..

[9]  Joe W. Duran,et al.  A General Expert System Design for Diagnostic Problem Solving , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.