Learning preferences and attitudes by multi-criteria overlap dominance and relevance functions

Abstract This paper proposes an interval-valued multi-criteria method for learning preferences and attitudes, identifying priorities with maximal robustness for decision support. The method is based on the notion of weighted overlap dominance, formalized by means of aggregation operators and interval-valued fuzzy sets. The procedure handles uncertainty by estimating the likelihood of dominance among pairs of alternatives, inducing an attitude-based system of dominance and indifference relations. This system allows conflicting situations of indifference/dependency to arise, which need to be resolved for properly identifying preferences under any attitude. In order to do so, relevance functions are examined over the whole system of relations, obtaining a weak preference order together with its associated attitude and robustness index. As a result, the proposed method allows learning preferences and attitudes, identifying the solutions with maximal robustness for intelligent decision support.