Practical Optimization Considerations For Diagnostic Knowledge Representation

AI in Equipment Maintenance and Support

Volume

Papers from the 1999 AAAI Spring Symposium

Contents

Using practical criteria for the selection of a diagnostic knowledge representation, businesses can optimize the choice to their specific business goals, processes, and organization structure. This optimization involves compromises, as there are tradeoffs among ease of acquiring the diagnostic knowledge, ease of maintenance, and ease of use in troubleshooting. We present a set of considerations in the form of questions we have found to be practical yet more thorough than the de facto short list of considerations used by most organizations. The answers to these questions can be used to derive requirements for the diagnostic knowledge representation. These considerations can be tailored to address particular business and knowledge engineering considerations. While the paper does not provide an exhaustive set of considerations, our experience is that questions like these provide a lower-risk basis on which decisions about diagnostic knowledge representation can be made.