
In this paper, we postulate computation as a key element in assuring the consistency of a family of aggregation functions so that such a family of operators can be considered an aggregation rule. In particular, we suggest that the concept of an aggregation rule should be defined from a computational point of view, focusing on the computational properties of such an aggregation, i.e., on the manner in which the aggregation values are computed. The new algorithmic definition of aggregation we propose provides an operational approach to aggregation, one that is based upon lists of variable length and that produces a solution even when portions of data are inserted or deleted. Among other advantages, this approach allows the construction of different classifications of aggregation rules according to the programming paradigms used for their computation or according to their computational complexity.
Probabilidades, aggregation rule, aggregation operator, Reasoning under uncertainty in the context of artificial intelligence, aggregation function
Probabilidades, aggregation rule, aggregation operator, Reasoning under uncertainty in the context of artificial intelligence, aggregation function
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