Abstract: Model-based diagnosis is an intelligent inference technology in order to overcome the serious defects of the first generation of diagnostic system. With the consistent development of relevant work, it is a significant branch of AI at present. However, most of the researches focus on the process of finding out the diagnosis. The process of detecting the diagnosis ensures the minimality of the final solution. It is also a crucial step in the problem. The traditional process of minimality-checking of diagnosis is to compare the new diagnosis with the ones in the existing diagnosis set, checking whether there is a superset or subset of the new diagnosis. The disadvantage of the traditional process is that as the number of diagnosis increases, the difficulty of detection increases gradually, and the time-consuming increases. To solve the problem, we propose a new method of minimality-checking of diagnosis based on subset consistency detection: subset consistency detection (SCD) method. Avoiding the influence of increasing the diagnosis set size, we determine the minimality of diagnosis through the consistency detection of a few subsets of the diagnosis. Our method can be applied to many efficient diagnostic algorithms such as grouped diagnosis (GD) and abstract circuit diagnosis (ACDIAG), and the efficiency of the algorithms is improved by SCD method.

Key words: model-based diagnosis, subset consistency, minimal diagnosis, SAT, conjunctive normal form