In many real–world decisions (either professional or everyday ones), we experience the fusion of full alternative and less relevant one, which leads to an OR ELSE evaluation (e.g., spacious basement or else spacious attic). This aggregation is so-called intensified disjunction, because the optional alternative might be more or less (but not fully) relevant. The dual observation holds for the evaluation by constraints and wishes expressed by an AND IF POSSIBLE aggregation (e.g., low price and if possible short travel distance). This aggregation is so-called relaxed conjunction, because the optional constraint (or wish) might more or less (but not fully) restrict the overall condition for evaluation. In order to cover these evaluations, we should apply the suitable aggregation functions. The talk is focused on the demonstration of the most suitable ways for solving these tasks also considering the usual human uncertainty. By applying machine learning, we are able to adjust parameters of aggregation functions to meet as close as possible our expectation. Finally, the asymmetric evaluation is illustrated on examples.