Abstract
A large Knowledge System operating for a long time almost inevitably becomes 'polluted' by wrong data that make the system inconsistent. Despite this fact, a sizeable part of the system remains unpolluted, and retains useful information. It is widely adopted that a maximally consistent subset of a system (mc-subset) contains a significant portion of unpolluted data. So, determining mc-subsets is a necessary step towards reasoning with inconsistent knowledge. We consider extensions of the MAX-SAT problem, investigate characteristic features of mc-subsets, present algorithms for computing all or major mc-subsets of inconsistent sets of clauses, and, report results of experiments evaluating parameters of mc-subsets.
| Original language | English |
|---|---|
| Pages (from-to) | 25-46 |
| Number of pages | 22 |
| Journal | Journal of Experimental and Theoretical Artificial Intelligence |
| Volume | 15 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2003 |
Keywords
- Algorithms
- Knowledge pollution
- MAX-SAT extensions
- Maximally consistent subsets
- Reasoning with inconsistency
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