Computational approaches to finding and measuring inconsistency in arbitrary knowledge bases

• Presented existing MUS generalization method to finding MISes.
• Proposed new optimized MUS transformation method to finding MISes.
• Implemented both methods and various inconsistency measures in a tool called MIMUS.
• Completed experimental evaluation of MIMUS using random arbitrary knowledge bases.
• MIMUS remains viable for large and complex random inconsistent knowledge bases.

There is extensive theoretical work on measures of inconsistency for arbitrary formulae in knowledge bases. Many of these are defined in terms of the set of minimal inconsistent subsets (MISes) of the base. However, few have been implemented or experimentally evaluated to support their viability, since computing all MISes is intractable in the worst case. Fortunately, recent work on a related problem of minimal unsatisfiable sets of clauses (MUSes) offers a viable solution in many cases. In this paper, we begin by drawing connections between MISes and MUSes through algorithms based on a MUS generalization approach and a new optimized MUS transformation approach to finding MISes. We implement these algorithms, along with a selection of existing measures for flat and stratified knowledge bases, in a tool called mimus. We then carry out an extensive experimental evaluation of mimus using randomly generated arbitrary knowledge bases. We conclude that these measures are viable for many large and complex random instances. Moreover, they represent a practical and intuitive tool for inconsistency handling.