Model-based diagnosis is an important problem in the field of artificial intelligence.In model-based diagnosis

how to get the minimal conflict sets is a well-known problem with extensive applications.In this paper

according to the characteristics of the conflict sets

we use the enumeration tree to reconstruct the process of solving conflict sets and then design a reverse depth algorithm based on the previous algorithm CSISE-Tree.Firstly

this proposed reverse depth search algorithm can reduce as many memory spaces as possible when obtaining some conflict sets

while CSISE-Tree have to expend some unnecessary memory spaces in this case

where the consume of memory spaces exponentially grows with the number of circuit elements.Secondly

compared with CSISE-Tree

our algorithm can effectively cut down the number of calling the SAT(Boolean SATisfiability problem) solver by pruning some non-minimal conflict sets and non-conflict sets.The experimental results show that our algorithm performs better than its competitor CSISE-Tree in terms of run time in most instances.More importantly

our algorithm avoids the memory explosion when solving some large instances.