Expert critics have been built to critique human performance in various areas such as engineering design, decision making, etc. We suggest that critics can also be useful in the building and use of knowledge based design systems (KBDSs). Knowledge engineers elicit knowledge from domain experts and build a knowledge based design system. The system generates designs. The amount of knowledge the system possesses and the way it applies the knowledge directly influence the performance of its designs. Therefore, critics are proposed to assist in: acquiring sufficient knowledge for constructing a desirable system; and applying proper knowledge to generating designs. Methodologies of equipping a KBDS with critics are developed. Our practice in building and using a KBDS shows the applicability and capability of these critics.>

Reasoning under uncertainty is one of the most important challenges in expert systems and some other branches of AI. Computational efficiency is a primary problem in implementing any practical system. In order to improve computational efficiency, several methods have been proposed to exploit the parallelism inherent in reasoning under uncertainty. However, some of these models can be used only in the case of singly connected networks, and one allows only one direction of reasoning. A parallel reasoning method based on the minimum cross entropy principle and the concept of recursive causal models is proposed to avoid the disadvantages of the methods.>