Using Syntactic Possibilistic Fusion for Modeling Optimal Optimistic Qualitative Decision
نویسندگان
چکیده
This paper describes the use of syntactical data fusion to computing possibilistic qualitative decisions. More precisely qualitative possibilistic decisions can be viewed as a data fusion problem of two particular possibility distributions (or possibilistic knowledge bases): the first one representing the beliefs of an agent and the second one representing the qualitative utility. The proposed algorithm computes a pessimistic optimal decisions based on data fusion techniques. We show that the computation of optimal decisions is equivalent to computing an inconsistency degree of possibilistic bases representing the fusion of agent’s beliefs and agent’s preferences. Keywords— Data Fusion, Decision theory, Pessimistic Criteria, Possibilistic Logic.
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