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Expert systems are problem solvers for specialized domains of competence in whieh effective problem solving normally reąuires human expertise. The transition of expert systems teehnology from research laboratories to software development centers highlighted the fact that the ąuality assurance for expert system is a very important issue for most real-word próbiems. Although the basie verification conoepts are shaned by software engineering and knowledge engineering, verifieation methods of conventional software are not directly applicable to expert systems and the new, spedfie methods of verifieation are reąuired. The mainaimofthis workisto present our own rule base verification method. In our’ opinion tlie deeision units conception allows us to consider differentverification and validationissuestogether. Thanks to properties of the deeision units we ean perform different verification and validation actions during knowledge base development and realization.

Expert systems are problem solvers for specialized domains of competence in which effective (noblem sohingnormally reąuires human expertise. Tlie transition of expert systems teehnology froin research labo ratories to software development centers highlighted tlie fact thatthe ąuality assurance for expert system is a very important issue for most real-w^Tord problems. Although the basie yerification concepts are shared by software engineeriięj and knowledge eiwpneering, rerifieation methods of eonrentional softw^rare are not directly applieable to expert systems and the new, specific methods of yerification are reąuired* The main aim of this w^rork is to present our ow^rn rule base verification method. In our opinion the deeision units conception allows us to consider different yerification and yalidation issues together* Thanks to properties of the deeision units we can perform different yerification and yalidation actions during knowledge base development and realization*