Measures of inconsistencies in Fuzzy Answer Set Programming - application to Digital Forensic
The goal of this talk is to provide an introduction to measures of inconsistency under fuzzy answer set programming. On the one hand, fuzzy answer set programming is a suitable theoretical framework to deal with non-monotonic If-then inferences under fuzziness. Accordingly, in this framework we can model reasonings, deductions and argumentations where the addition of new facts may change completely the set of conclusions. Moreover, it is able to deal with fuzzy notions. Fuzzy notions are those which may be true in some degree as “to be a suspect” in the sense a person may be more or less suspect of a crime.
On the other hand, the measures of inconsistency aim at determining how much contradictions there are in a data base. Concerning digital forensic, inconsistencies is a common feature with emerges in several situations, e.g., testimonies of witnesses, locations of suspects or in the time-chain. In this respect, the treatment of inconsistencies in databases might provide, in the context of digital forensic, interesting trails or evidences oriented to the construction of a coherent recreation of a crime.