An approach to situation detection is to represent the world objects in a hierarchical scene graph, and model sensory events as influences on those objects. This can allow for the identification of effects of causality and the influence it has on multiple related objects, such as the propagation of effects to their dependencies, e.g pushing a box with a pen in it, will also move the pen relative to the box. In this way, when a stimulus affects an object, the resulting change/response can be observed in its dependencies.
This should make it possible to identify which objects should be actively observed without checking all of them in the scene, eg, pushing the box means only the box and its dependent might need to be checked, saving computation time. While this is useful, there is likely still a need to be able to determine when objects that are still important, i.e make up the situation, but have not actually changed in response to the stimuli (universals).
The basic premise is that the scene graph is checked for differences caused by stimuli, and these changes can be used to detect situational boundaries. An illustration of this approach is outlined in Figure 12.
Another possibility is that this hierarchical representation might serve as a means to aggregate, group and generalise the effects that multiple changes in the scene have. This could help analyse consistency or help derive boundaries by forming level of detail calculations, where a lower level of detail might ignore smaller, frequent or consistent changes and focus on bigger changes in order to generalise a situation boundary at that level. This is essentially the formation of abstractions from details.
It would also be important to determine the level of granularity or resolution of the time-based observational window, as this is likely to affect how situational boundaries are captured, for example.