Followup to: Structural representation of uncertainty, Continuous balancing of changing structure.
Labels allow to represent states of knowledge in the most compact form. Expressive power of structural contexts and whole scenes allows to construct representations of elaborate combinations of previously encountered states of knowledge. These more complex representations are harder to manage than simple labels, and so when certain structural pattern (or map) becomes common enough, it can be assigned a new unique label of its own.
New labels allow to compactly represent frequently encountered states of knowledge, to form a language adapted to the environment. Multiple generations of labels can represent the most salient aspects of bigger and bigger structures in the scene. Smaller representation allows more robust processing. Structure restoration can function with fewer errors because structures that need to be restored become smaller. Maps can capture more global concepts in the scene without needing to consider more labels at the same time, because common combinations of labels are summarized by new labels. New labels form a basis for new levels of representations for structures that are already represented in the scenes.
A label by itself is good for nothing: if it isn’t in any map, it’ll never get restored. If a new label appears once in an ordinary context, it’ll never be restored again, because this context is matched by old maps better than a new map that also includes a new label.
A new label can get remembered if it appears in a novel structural context consisting of old labels. New maps that capture this new context can be restored in the future by right combinations of the old labels, and as a result restore the new label. From now on, the new label appears in all scenes containing this novel structural context, at the same time new maps representing this context do. As a result, it becomes possible to represent this context and associated maps just by the new label, and this label gets learned by other maps to reflect the presence of regularity represented by it. It’s no longer owned by the context in which it was bootstrapped and with which it was originally associated, and in the future it can even get completely disassociated from it, gradually shifting its semantics elsewhere.
Thus, it’s unnecessary to create a separate algorithm to manage new labels and rigidly assign them to maps they are supposed to represent, map learning takes care of it. It’s sufficient to create a new label for each new map, and if it turns out to be useful, it’ll get learned by other maps. Relatively useless labels get abstracted out of maps, the same way relatively useless maps get discarded or merged with other maps.
Posted by Vladimir Nesov 