Improved algorithms for deriving all minimal conflict sets in model-based diagnosis

Model-based diagnosis is one of the active branches of Artificial Intellgence. Conflict recognition, aiming at generating all minimal conflict sets (MCSs), and candidate generation, aiming at generating all minimal hitting sets (MHSs), are of the two important steps towards to the final diagnosis results. Firstly an SE-tree based algorithm (CSSE-tree) for deriving all MCSs is given. Then a concept of inverse SE-tree (ISE-tree) is put forward, and an ISE-tree based algorithm (CSISE-tree) for deriving all MCSs is presented as well. Considering the similarity of generation of all MCSs and all MHSs for the collection of all MCSs, a uniform framework for deriving all MCSs and MHSs is proposed, too. Experimental results show that our algorithms have better efficiency than others in some situations.