Operations on Conversational Mind-graphs

Mind-graphs define an associative-adaptive concept of managing information streams, like for example words within a conversation. Being composed of vertices (or cells; representing external stimuli like words) and undirected edges (or connections), mind-graphs adaptively reflect the strength of simultaneously occurring stimuli and allow a self-regulation through the interplay of an artificial ’fever’ and ’coldness’ (capacity problem). With respect to this, an interesting application scenario is the merge of information streams that derive from a conversation of k conversing partners. In such a case, each conversational partner has an own knowledge and a knowledge that (s)he shares with another. Merging the own (inside) and the other (outside) knowledge leads to a situation, where things like e.g. trust can be decided. In this paper, we extend this concept by proposing extended mind-map operations, dealing with the merge of sub-mind-graphs and the extraction of mind-graph skeletons.