An Adjustment Function For Dealing With Similarities (COMMA 2020)

The paper investigates gradual semantics that are able to deal with similarity between arguments. Following the approach that defines semantics with evaluation methods, i.e., a couple of aggregation functions, the paper argues for the need of a novel function, called \textit{adjustment function}. The latter is responsible for taking into account similarity when it is available. It aims at reducing the strengths of attackers according to the possible similarities between them. The reason is that similarity is seen as redundancy that should be avoided, otherwise a semantics may return inaccurate evaluations of arguments. The paper proposes a novel adjustment function that is based on the well-known weighted h-Categorizer, and investigates its formal properties.