Musa J. Jafar
Historically, knowledge-base validation and verification (V&V) has been carded out manually. It is the process where the knowledge engineers and their experts review the knowledgebase and look for syntactic and semantic errors, build decision trees, check for the logical connectivity of the rules, run as many test cases as possible, and watch for mismatches between the system’s and experts’ output. This is a timeconsuming, error-prone process; it does not guarantee f'mding all errors. Manually built decision trees are tedious and need to be redrawn after each change in the knowledge-base. Finally, running test cases is a hit-or-miss activity, it tells if the system has errors, it does not tell where an error occurred or what caused it.